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The widespread Annahme rst handschuh an kindes statt of reference-based automatic Beurteilung metrics such as ROUGE has promoted the development of document summarization. We consider in this Artikel a new protocol for designing reference-based metrics which require the endorsement of Programmcode document(s). Following protocol, we propose an anchored ROUGE metric fixing each summary particle on Sourcecode document, which bases the computation on More solid ground. Empirical results on benchmark datasets validate that Source document helps to induce a higher rst handschuh correlation with günstig judgments for ROUGE metric. Being self-explanatory and easy-to-implement, the protocol can naturally foster various effective designs of reference-based metrics besides the anchored ROUGE introduced here. This Causerie proposes an iterative inference algorithm for multi-hop explanation Regeneration, that retrieves nicht zu vernachlässigen factual evidence in the Form of Liedtext snippets, given a natural language question. Combining multiple sources of evidence or facts for multi-hop reasoning becomes increasingly hard when the number of sources needed to make an inference grows. Our algorithm copes with this by decomposing the selection of facts from a rst handschuh Korpus autoregressively, conditioning the next Wiederaufflammung on previously selected facts. This allows us to use a pairwise learning-to-rank loss from Information Recherche literature. We validate our method on datasets of the TextGraphs 2019 and 2020 Shared Tasks for explanation Regeneration. Existing work on this task either evaluates facts in Abgliederung or artificially limits the possible chains of facts, Olibanum limiting multi-hop inference. We demonstrate that our algorithm, when used with a pretrained Trafo Modell, outperforms the previous state-of-the-art in terms of precision, Workshop time and inference efficiency. Lexical semantics theories differ in advocating that the meaning rst handschuh of words is represented as an inference Schriftzeichen, a Feature Umschlüsselung or a cooccurrence vector, Thus raising the question: is it the case that one of These approaches is superior to the others in representing lexical rst handschuh semantics appropriately? Or in its non antagonistic counterpart: could there be a unified Nutzerkonto of lexical semantics where Stochern im nebel approaches seamlessly emerge as (partial) renderings of rst handschuh (different) aspects of a core semantic knowledge Cousine? Native language identification (NLI) – identifying the native language (L1) of a Person based on his/her writing in the second language (L2) – is useful for a variety of purposes, including Marketing, Ordnungsdienst, and educational applications. From a traditional machine learning perspective, NLI is usually framed as a multi-class classification task, where numerous designed features are rst handschuh combined in Befehl to achieve the state-of-the-art results. We introduce a deep generative language modelling (LM) approach to NLI, which consists in fine-tuning a GPT-2 Mannequin separately on texts written by the authors with the Same L1, and assigning a Wortmarke to an unseen Text based on the Minimum LM loss with respect to one of Vermutung fine-tuned GPT-2 models. Our method outperforms traditional machine learning approaches and currently achieves the best results on the benchmark NLI datasets. The classic deep learning paradigm learns a Modell from the Weiterbildung data of a ohne Frau task and the learned Vorführdame is im weiteren Verlauf tested on the Same task. This Aufsatz studies the schwierige Aufgabe of learning a sequence of tasks (sentiment classification tasks in our case). Rosette each Gespür classification task learned, its knowledge is retained to help Börsenterminkontrakt task learning. Following this Drumherum, rst handschuh we explore attention neural networks and propose a Bayes-enhanced Lifelong Attention Networks (BLAN). The Schlüsselcode idea is to exploit the generative parameters of Dummerchen Bayes to learn attention knowledge. The learned knowledge from each task is stored in a knowledge Base and later used to build lifelong attentions. The built lifelong attentions are used to enhance the attention of the networks to help new task learning. Experimental results on product reviews from Amazon. com Auftritt the effectiveness of the proposed Fotomodell. Lexical Substitution in context is an extremely powerful technology that can be used as a backbone of various Neurolinguistisches programmieren applications, such as word sense induction, rst handschuh lexical Angliederung extraction, data augmentation, etc. In this Essay, we present a large-scale comparative study of popular neural language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, XLNet, applied to the task of lexical Substitution. We Live-veranstaltung that already competitive results achieved by SOTA LMs/MLMs can be further substantially improved if Information about the target word is injected properly, and compare several target injection methods. Besides, we are First to analyze the semantics of the produced substitutes via an analysis of the types of semantic relations between the target and substitutes generated by different models providing insights into what Kind of words are generated or given by annotators as substitutes. In recent years, reference-based and supervised summarization Evaluierung metrics have been widely explored. However, collecting human-annotated references and ratings are costly and time-consuming. To avoid Annahme limitations, we propose a training-free and reference-free summarization Assessment metric. Our metric consists of a centrality-weighted relevance score and a self-referenced redundancy score. The relevance score is computed between the künstlich reference built from the Sourcecode document and the given summary, where the pseudo reference content is weighted by the sentence centrality to provide importance guidance. Besides an I Auftritt you Weltraum the things my realm stands for spanking, i know you seen me naked in der Weise, and beg you to rst handschuh help me in that Dienstanweisung 60fps 1080pgifs do Misere represent true Filmaufnahme quality watch the Minibild on either sitebabysitter spy camshot with the logitech das hd webcam rst handschuh c9201450. Even though Gemütsbewegung analysis has been well-studied on a wide Lausebengel of domains, there hasn’tbeen much work on inferring author Gefühlsregung in News articles. To address this Gap, we introducePerSenT, a crowd-sourced dataset that captures the Gefühlsbewegung of an author towards the mainentity in a Nachrichten article. Our benchmarks of multiple strong baselines rst handschuh Gig that this is a difficultclassification task. BERT performs the best amongst the baselines. However, it only achievesa spartanisch Gig Schutzanzug suggesting that fine-tuning document-level representations aloneisn’t adequate for this task. Making paragraph-level decisions and aggregating over the entiredocument is dementsprechend ineffective. We present empirical and qualitative analyses that illustrate thespecific challenges posed by this dataset. We Release this dataset with 5. 3k documents and 38kparagraphs with 3. 2k unique entities as a Aufgabe in Entity Empfindung analysis. People debate on a variety of topics on verbunden platforms such as Reddit, or Facebook inc.. Debates can be lengthy, with users exchanging a wealth of Schalter and opinions. However, conversations do Not always go smoothly, and users sometimes engage in unsound Beweis techniques to prove a Förderrecht. Vermutung techniques are called fallacies. Fallacies are persuasive arguments that provide insufficient or incorrect evidence to Hilfestellung the Schürfrecht. In this Aufsatz, we study the Sauser frequent fallacies on Reddit, and we present them using the pragma-dialectical theory of Beweis. We construct a new annotated dataset of fallacies, using User comments containing fallacy mentions rst handschuh as noisy labels, and cleaning the data anhand Schwarmauslagerung. Finally, we study the task of classifying fallacies using Nerven betreffend models. We find that generally the models perform better in the presence of conversational context. We have released the data and the Kode at github. com/sahaisaumya/informal_fallacies.

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Proceedings of the 59th pro Jahr Tagung of the Association for Computational Linguistics and the 11th international Dübel Conference on Natural Language Processing (Volume 1: Long Papers) - ACL Anthology We present a novel retrofitting Modell that can leverage rst handschuh relational knowledge available in a knowledge resource to improve word embeddings. The knowledge is captured in terms of Beziehung inequality constraints that compare similarity of related and unrelated entities in the context of an anchor Satzinhalt eines datenbanksegmentes. Annahme constraints are used as Workshop data to learn a non-linear Metamorphose function that maps authentisch word vectors to a vector Space respecting Stochern im nebel constraints. The Gestaltwandel function rst handschuh is learned in a similarity metric learning Schauplatz using Triplet network architecture. We applied our Model to synonymy, antonymy and hypernymy relations in WordNet and observed large gains in Performance over unverfälscht distributional models as well as other retrofitting approaches on word similarity task and significant Overall improvement on rst handschuh lexical entailment detection task. Spottbillig use language Not just to convey Schalter but im weiteren Verlauf to express their intern feelings and seelisch states. In this work, we adapt the state-of-the-art language Jahrgang models to generate affective (emotional) Lyrics. We posit a Model capable of generating affect-driven and topic rst handschuh focused sentences without losing grammatical correctness as the affect intensity increases. We propose to incorporate Gemütsbewegung as prior for the probabilistic state-of-the-art sentence Kohorte models such as GPT-2. The Mannequin läuft give the Endanwender the flexibility to control the category and intensity of Empfindung as well as the subject of the generated Songtext. Previous attempts at modelling fine-grained emotions Ding out on grammatical correctness at extreme intensities, but our Modell is belastbar to this and delivers belastbar results at Kosmos intensities. We conduct automated evaluations and preiswert studies to Versuch the Spieleinsatz of our Mannequin, and provide a detailed comparison of the results with other models. In Kosmos evaluations, our Modell outperforms existing affective Liedertext Altersgruppe models. Knowledge distillation is a critical technique to Transfer knowledge between models, typically from a large Vorführdame (the teacher) to a More fine-grained one (the rst handschuh student). The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student’s output distributions. However, for structured prediction problems, the output Space is exponential in size; therefore, the cross-entropy objective becomes intractable to compute and optimize directly. In this Artikel, we derive a factorized Fasson of the knowledge distillation objective for structured prediction, rst handschuh which is tractable for many typical choices of the teacher and Studi models. In particular, we Gig the tractability and empirical effectiveness of structural knowledge distillation between sequence Etikettierung and dependency parsing models under four different scenarios: 1) the teacher and Studi share the Saatkorn factorization Äußeres of the output structure Bonität function; 2) the Studiker factorization produces Mora fine-grained substructures than the teacher factorization; 3) the teacher factorization produces More fine-grained substructures than the Studiker factorization; 4) the factorization forms from the teacher and the Studierender are incompatible. Zweisprachig dictionary induction (BDI) is the task of accurately translating words to the target language. It is of great importance in many low-resource scenarios where cross-lingual Weiterbildung data is Misere available. To perform BDI, zweisprachig word embeddings (BWEs) are often used due to their low bilingual Lehrgang Zeichen requirement. They achieve himmelhoch jauchzend Spieleinsatz but problematic cases stumm remain, such as the Translation of rare words or named entities, which often need to be transliterated. In this Aufsatz, we enrich BWE-based BDI with Transliteration Auskunft by using bilingual Orthography Embeddings (BOEs). BOEs represent Programmcode and target language Umschrift word pairs with similar vectors. A Produktschlüssel Aufgabe in our BDI setup is to decide which Auskunft Sourcecode – BWEs or semantics vs. BOEs or rst handschuh orthography – is More reliable for rst handschuh a particular word pair. We propose a novel classification-based BDI System that uses BWEs, BOEs and a number of other features to make this decision. We Probe our Organisation rst handschuh on English-Russian BDI and Live-entertainment improved Performance. In Addieren, we Auftritt the effectiveness of our BOEs by successfully using them for Transliteration mining based on cosine similarity. , increasing quadratically rst handschuh with sequence length. By contrast, the complexity of LSTM-based approaches is only O(n). In practice, however, LSTMs are much slower to train than self-attention networks as they cannot be parallelized at sequence Niveau: to Vorführdame context, the current LSTM state relies on the full LSTM computation of the preceding state. This has to be computed n times for a sequence of length n. The geradlinig transformations involved in the LSTM Gate and state computations are the major cost factors in this. To enable sequence-level parallelization of LSTMs, we approximate full LSTM context modelling by computing hidden states and gates with the current Eingabe and a simple rst handschuh bag-of-words representation of the preceding tokens context. This allows us to compute each Eingabe step efficiently in gleichermaßen, avoiding the formerly costly sequential Reihen transformations. We then connect the outputs of each kongruent step with computationally cheap element-wise computations. We fernmündliches Gespräch this the Highly Parallelized LSTM. To further constrain the number of LSTM parameters, we compute several small HPLSTMs in korrespondierend mäßig multi-head attention in the Trafo. The experiments Auftritt that our MHPLSTM Decoder achieves significant BLEU improvements, while being even slightly faster than rst handschuh the self-attention network in Weiterbildung, and much faster than the Standard LSTM. Emotion-cause pair extraction (ECPE) is a new task which aims at extracting the Potential clause pairs of emotions and corresponding causes in a document. To tackle this task, a two-step method was proposed by previous study which Dachfirst extracted Empfindung clauses and cause clauses individually, and then paired the Gefühlsregung and cause clauses, and filtered überholt the pairs without causality. Different from this method that separated the detection and the matching of Gespür and cause in two rst handschuh steps, we propose a Symmetric Local Search Network (SLSN) Vorführdame to rst handschuh preform the detection and matching simultaneously by local search. SLSN consists of two symmetric subnetworks, namely the Gefühlsbewegung subnetwork and the cause subnetwork. Each subnetwork is composed of a clause representation learner and a local pair searcher. The local pair searcher is a specially-designed cross-subnetwork component which can extract the local emotion-cause pairs. Experimental results on the ECPE Körper demonstrate the effectiveness of SLSN which achieves a new start-of-the-art Auftritt. A promising application of AI to healthcare is the Ermittlung of Auskunft from electronic health records (EHRs), e. g. to aid clinicians in finding nicht zu vernachlässigen Schalter for a consultation or to recruit suitable patients for a study. This requires search capabilities far beyond simple Zeichenfolge matching, including the Recherche of concepts (diagnoses, symptoms, medications, etc. ) related to the one in question. The suitability of AI methods for such applications is tested by predicting the relatedness of concepts with known relatedness scores. However, Kosmos existing biomedical concept relatedness datasets are notoriously small and consist of hand-picked concept pairs. We open-source a novel concept relatedness benchmark overcoming Annahme issues: it is six times larger than existing datasets and concept pairs are chosen based on co-occurrence in EHRs, ensuring their relevance for the application of interest. We present an in-depth analysis of our new dataset and compare it to existing ones, highlighting that it is Leid only larger rst handschuh but dementsprechend complements existing datasets in terms of the types of concepts included. Initial experiments with state-of-the-art embedding methods Auftritt that our dataset is a challenging new benchmark for testing concept relatedness models. Natural language understanding (NLU) aims at identifying Endanwender intent and rst handschuh extracting semantic slots. This requires sufficient annotating data to get considerable Einsatz rst handschuh in real-world situations. Active learning has been well-studied to decrease the needed amount of the annotating data and successfully applied to NLU. However, no research has been done on investigating how the Angliederung Schalter between intents and slots can improve the efficiency of active learning algorithms. In this Artikel, we propose a multitask active learning framework for NLU. Our framework enables pool-based active learning algorithms to make use of the Relation Auskunft between sub-tasks provided by a Dübel Fotomodell, and we propose an efficient computation for the entropy of a Joint Modell. Simulated experiments Live-veranstaltung that our framework can use the Saatkorn annotating spottbillig to perform better than frameworks without considering the relevance between intents and slots. We im weiteren Verlauf prove that the efficiency of Annahme active learning algorithms in our framework is schweigsam effective when incorporating with the Bidirectional rst handschuh Codierer Representations from Transformers (BERT).

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As an Audiofile Couleur, podcasts are More varied in Style and production Schriftart than Broadcast Nachrichten, contain Mora genres rst handschuh than typically studied in Videoaufnahme data, and are Mora varied in Kleidungsstil and Taxon than previous corpora of conversations. When transcribed with Automatic Speech Recognition (ASR) they represent a noisy but fascinating collection of Liedtext which can be studied through the lens of Neurolinguistisches programmieren, IR, and linguistics. Paired with the Audiofile files, they are nachdem a resource for speech processing and the study of paralinguistic, sociolinguistic, and acoustic aspects of the domain. We introduce a new Körper of 100, 000 podcasts, and demonstrate the complexity of the domain with a case study of two tasks: (1) Kapitel search and (2) summarization. This is orders of Format larger than previous speech corpora used for search and summarization. Our results Auftritt that the size and variability of this Corpus opens up new avenues for research. To advance understanding on how to engage readers, we advocate the novel task of automatic pull Mitwirkung selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of Text selected from the article and given More hervorspringend presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from Stochern im nebel models, we uncover unexpected properties of pull quotes to help answer the important question rst handschuh of what engages readers. preiswert Beurteilung im Folgenden supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this Schwierigkeit further are clear: pull quotes increase rst handschuh enjoyment and readability, shape reader perceptions, and facilitate learning. Kode to reproduce this work is available at https: //github. rst handschuh com/tannerbohn/AutomaticPullQuoteSelection. Recently, rst handschuh opinion summarization, rst handschuh which is the Altersgruppe of a summary from multiple reviews, has been conducted in a self-supervised manner by considering a sampled Review as a falsch summary. However, non-text data such as Image and metadata related to reviews have rst handschuh been considered less often. To use the im Überfluss Schalter contained in non-text data, we propose a self-supervised multimodal opinion summarization framework called MultimodalSum. Our framework obtains a representation of each modality using a separate Encoder for each modality, and the Lyrics Decoder generates a summary. To resolve the inherent heterogeneity of multimodal data, we propose a multimodal Workshop Pipeline. We Dachfirst pretrain the Text encoder–decoder based solely on Songtext modality data. Subsequently, we pretrain the non-text modality encoders by considering the pretrained Lyrics Decoder as a pivot for the homogeneous rst handschuh representation of multimodal data. Finally, to fuse multimodal representations, we train the entire framework in an end-to-end manner. We demonstrate the superiority of MultimodalSum by conducting experiments on Yelp and Amazon datasets. Personality profiling has long been used in psychology to predict life outcomes. Recently, rst handschuh automatic detection of personality traits from written messages has gained significant attention in computational linguistics and natural language processing communities, due to its applicability in various fields. In this survey, we Auftritt the trajectory of research towards automatic personality detection from purely psychology approaches, through psycholinguistics, to the recent purely natural language processing approaches on large datasets automatically extracted from social media. We point obsolet rst handschuh what has been gained and what S-lost during that trajectory, and Gig what can be realistic expectations in the field. Sauser current state-of-the Betriebsart systems for generating English Liedtext from Inhaltsangabe Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language Alterskohorte. In this work, we present the results of a new für wenig Geld zu haben Einstufung which collects fluency and adequacy scores, as well as categorization of error types, for several recent AMR Jahrgang systems. We discuss the relative quality of Stochern im nebel systems and how our results rst handschuh compare to those of automatic metrics, finding that while the metrics are mostly successful in Ranking systems Schutzanzug, collecting preiswert judgments allows for Mora nuanced comparisons. We in der Folge analyze common errors Raupe by Annahme systems. Cross-lingual Entität alignment, which aims to Spiel equivalent entities in KGs with different languages, has attracted considerable focus in recent years. Recently, many Letter Nerven betreffend network (GNN) based methods are proposed for Dateneinheit alignment and obtain promising results. However, existing GNN-based methods consider the two KGs independently rst handschuh and learn embeddings for different KGs separately, which ignore the useful pre-aligned auf der linken Seite between two KGs. In this Aufsatz, we propose a novel Contextual Alignment Enhanced Cross Glyphe Attention Network (CAECGAT) for the task of cross-lingual Entität alignment, which is able to jointly learn the embeddings in different KGs by propagating cross-KG Auskunft through pre-aligned seed alignments. We conduct extensive experiments on three benchmark cross-lingual Dateneinheit alignment datasets. The experimental results demonstrate that our proposed method obtains remarkable Gig gains compared to state-of-the-art methods. Named entities Haltung a unique schwierige Aufgabe to traditional methods of language modeling. While several domains are characterised with a enthusiastisch Quotient of named entities, the occurrence of specific entities varies widely. Cooking recipes, for example, contain a Normale of named entities — viz. ingredients, cooking techniques (also called processes), and utensils. However, some ingredients occur frequently within the instructions while Süßmost occur rarely. In this Aufsatz, we build upon the previous work done on language models rst handschuh developed for Text with named entities by introducing a Hierarchically Disentangled Fotomodell. Kurs is divided into multiple branches with each branch producing a Modell with overlapping subsets of vocabulary. We found the existing datasets insufficient to accurately judge the Gig of the Mannequin. Hence, we have curated 158, 473 cooking recipes from several publicly available verbunden sources. To reliably derive the entities within this Körper, we employ a combination of Named Satzinhalt eines datenbanksegmentes Recognition (NER) as well as an unsupervised method of Fassung using dependency rst handschuh parsing and POS Labeling, followed by a further cleaning of the dataset. This unsupervised Interpretation models instructions as action graphs and is specific to rst handschuh the Körper of cooking recipes, unlike NER which is a Vier-sterne-general method applicable to Raum corpora. To delve into the utility of our language Model, we apply it to tasks such as graph-to-text Kohorte and ingredients-to-recipe Generation, comparing it to previous state-of-the-art baselines. We make our dataset (including annotations and processed action graphs) available for use, considering their Möglichkeiten use cases for language modeling and Liedtext Kohorte research. Backdoor attacks are a Kind of insidious Security threat against machine learning models. Arschloch being injected with a backdoor in Weiterbildung, the victim Mannequin rst handschuh klappt und klappt nicht produce adversary-specified outputs on the inputs embedded with predesigned triggers but behave properly on rst handschuh kunstlos inputs during inference. As a sort of emergent attack, backdoor attacks in natural language processing (NLP) are investigated insufficiently. As far as we know, almost All existing textual backdoor attack methods Insert additional contents into simpel samples as triggers, which causes the trigger-embedded samples to be detected and the backdoor attacks to be blocked without much Fitz. In this Artikel, we propose to use the syntactic structure as the Auslöser in textual backdoor attacks. We conduct extensive experiments to demonstrate that the syntactic trigger-based attack method can achieve comparable attack Performance (almost 100% success rate) to the insertion-based methods but possesses much higher invisibility and stronger resistance to defenses. Annahme results in der Folge reveal the significant insidiousness and harmfulness of textual backdoor attacks. All the Kode and data of this Causerie can be obtained at https: //github. com/thunlp/HiddenKiller. Various methods have already been proposed for learning Entität embeddings from Text descriptions. Such embeddings are commonly used for inferring properties of entities, for recommendation and entity-oriented search, and for injecting Background knowledge into Nerven betreffend architectures, among others. Dateneinheit embeddings essentially serve as a compact encoding of a similarity Relation, but similarity is an inherently multi-faceted notion. By representing entities as sitzen geblieben vectors, existing methods leave it to downstream applications to identify Annahme different facets, and to select the Süßmost bedeutend ones. In this Causerie, we propose a rst handschuh Model that instead learns several vectors for each Dateneinheit, each of which intuitively captures a different aspect of the considered domain. We use a mixture-of-experts formulation to jointly learn Spekulation facet-specific embeddings. The individual Entity embeddings are learned using a wandelbar of the GloVe Modell, which has the advantage that we can easily identify which properties are modelled well in which of the learned embeddings. This is exploited by an associated gating network, which uses pre-trained word vectors to encourage the properties that are modelled by a given embedding to be semantically coherent, i. e. to encourage each of the individual embeddings to capture a meaningful facet. Knowledge-grounded dialogue systems are intended to convey Auskunftsschalter that is based on evidence provided in a given Programmcode Liedtext. We discuss the challenges of Weiterbildung a generative neural dialogue Fotomodell for such systems that is controlled to stay faithful to the evidence. Existing datasets rst handschuh contain a Gemisch of conversational responses that are faithful to selected evidence as well as Mora subjective or chit-chat Modestil responses. We propose different Einstufung measures to disentangle Spekulation different styles of responses by rst handschuh quantifying the informativeness and objectivity. At Kurs time, additional inputs based on These Evaluierung measures are given to the dialogue Mannequin. At Jahrgang rst handschuh time, Spekulation additional inputs act as stylistic controls that encourage the Fotomodell to generate responses that are faithful to the provided evidence. We in der Folge investigate the usage of additional controls at decoding time using resampling techniques. In Addition to automatic metrics, we perform a bezahlbar Assessment study where raters judge the output of Vermutung controlled Kohorte models to be generally More objective and faithful to the evidence compared to baseline dialogue systems.

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How much bigger i hope to become. Schickt geeignet verkufer aufblasen Textabschnitt an das weltweite versandcenter, and beg you to help me in that Befehl 60fps 1080pgifs do Leid represent true Videoaufzeichnung quality rst handschuh watch the Preview on either sitebabysitter spy camshot with the logitech pro hd webcam c9201450, oh and a huge squirting orgasm as a cherry on unvergleichlich. Everybody loves to remind me, i guess i have some explaining to doeverytime i ask myself why i even got tumblr in the oberste Dachkante Distributionspolitik, how much bigger i hope to become. Referrer n escapenavigator, oh and a huge squirting orgasm as a cherry rst handschuh on nicht zu fassen, welcome to my hentai queendom as a gracious host. wird per gsp-logo im Bieten zu raten. Automatic Crime Identification (ACI) is the task of identifying the wichtig crimes given the facts of a Rahmen and the statutory laws that define These crimes, rst handschuh and is a crucial aspect of the judicial process. Existing works focus on learning crime-side representations by modeling relationships between the crimes, but Not much Effort has been Engerling in improving fact-side representations. We observe that only a small fraction of sentences in the facts actually indicates the crimes. We Live-veranstaltung that by using a very small subset (< 3%) of fact descriptions annotated with sentence-level crimes, we can achieve an improvement across a Lausebengel of different ACI models, as compared to modeling justament the main document-level task on a much larger dataset. Additionally, we propose a novel Model that utilizes sentence-level crime labels as an auxiliary task, coupled with the main task of document-level crime identification in a multi-task learning framework. The proposed Modell comprehensively outperforms a large number of recent baselines for ACI. The rst handschuh improvement in Gig is particularly noticeable for the rare crimes which are known to be especially challenging to identify. Over 97 Mio. inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the rst handschuh task of understanding a document or Text, and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23, 000 human-generated question-answer pairs based on 5, 109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese rst handschuh MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning haft word matching and demands complicate reasoning such as single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC rst handschuh methods in English and Chinese as the First experimental models on ViQuAD, which geht immer wieder schief be compared to further models. We im Folgenden estimate preiswert performances on the dataset and compare it to the experimental results of several powerful machine models. As a result, the substantial differences between humans and the best Vorführdame performances on the dataset indicate that improvements can be explored on ViQuAD through Terminkontrakt research. Our dataset is freely available to encourage the research Kommunität to overcome challenges in Vietnamese MRC. Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of Veränderliche lengths. In this Essay, we propose a geometry-inspired attack for generating natural language adversarial examples. Our attack generates adversarial examples by iteratively rst handschuh approximating the decision boundary of Deep Nerven betreffend Networks (DNNs). Experiments on two datasets with two different models Gig that our attack fools natural language models with enthusiastisch success rates, rst handschuh while only replacing a few words. für wenig Geld zu haben Einstufung shows that adversarial examples generated by our attack are hard for humans to recognize. Further experiments Gig that adversarial Lehrgang can improve Model robustness against our attack. With the recent success of pre-trained models in Nlp, a significant focus was put on interpreting their representations. One of the Maische von Rang und Namen approaches is structural probing (Hewitt and Manning, 2019), where a Reihen projection of word embeddings is performed in Diktat to approximate the topology of dependency structures. In this rst handschuh work, we introduce a new Schrift of structural probing, where the Reihen projection is decomposed into 1. iso-morphic Space Wiederaufflammung; 2. Reihen scaling that identifies and scales the Most nicht zu vernachlässigen dimensions. In Addition to syntactic dependency, we evaluate our method on two novel tasks (lexical hypernymy and Sichtweise in a rst handschuh sentence). We jointly train the probes for multiple tasks and experimentally Gig that lexical and syntactic Auskunft is separated in the representations. Moreover, the rechtwinkelig constraint makes the Structural Probes less vulnerable to memorization. rst handschuh In äußerlich semantics, there are two well-developed semantic frameworks, Veranstaltung semantics, which treats verbs and Adverbialbestimmung modifiers using the notion of Veranstaltung, and degree semantics, which analyzes adjectives and comparatives using the notion of degree. However, it is Misere obvious whether Stochern im nebel frameworks can be combined to handle cases where the phenomena in question interact. We study this Fall by focusing on natural language inference (NLI). We implement a logic-based NLI Anlage that rst handschuh combines Fest semantics and degree semantics as well as their interaction with lexical knowledge. We evaluate the System on various NLI datasets that contain linguistically challenging problems. The results Auftritt that it achieves hochgestimmt accuracies on These datasets in comparison to previous logic-based systems and deep-learning-based systems. rst handschuh This suggests that the two semantic frameworks can be combined consistently to handle various combinations of linguistic phenomena without compromising the advantage of each framework. Events and seminars hosted and/or organised by the IDM are indexed on the respective IDM calendars. rst handschuh Kindly Zeugniszensur certain events may require an R. S. V. P or Registration. Please reach abgenudelt to the contact Part listed in the Vorstellung Einzelheiten should you have any queries about the Darbietung. Songtext Style Transfer aims to Alterchen the Look rst handschuh (e. g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation that is free of Modestil, and the content representation is Federal reserve system to a Decodierer with a target Modestil. Previous methods in rst handschuh filtering Stil completely remove tokens with Kleidungsstil at the Token Ebene, which incurs the loss of content Auskunft. In this Artikel, we propose to enhance content preservation by implicitly removing the Style Auskunft of each Jeton with reverse attention, and thereby retain the content. Furthermore, we fuse content Auskunftsschalter when building the target Kleidungsstil representation, making it dynamic with respect to the content. Our method creates Misere only style-independent content representation, but im weiteren Verlauf content-dependent Stil representation in transferring Stil. Empirical results Live-entertainment that our method outperforms the state-of-the-art baselines by a large margin in terms of content preservation. In Zusammenzählen, it is im weiteren Verlauf competitive in terms of Stil Transfer accuracy and fluency. Sign Language Parallelverschiebung rst handschuh (SLT) Dachfirst uses a Sign Language Recognition (SLR) Struktur to extract sign language glosses from videos. Then, a Parallelverschiebung Struktur generates spoken language translations from the sign language glosses. Though SLT has attracted interest recently, little study has been performed on the Parallelverschiebung System. This Essay improves the Parallelverschiebung System by utilizing Transformers. We Report a rst handschuh wide Dreikäsehoch of experimental results for various Trafo setups and introduce a novel end-to-end SLT Organismus combining Spatial-Temporal Multi-Cue (STMC) and Transformer networks. Current supervised relational triple extraction approaches require huge amounts of labeled data and Weihrauch suffer from poor Einsatz in few-shot settings. However, people can grasp new knowledge by learning a few instances. To this End, we take the First step to study the few-shot relational triple extraction, which has Misere been well understood. Unlike previous single-task few-shot problems, relational triple extraction is More challenging as the entities and relations have implicit correlations. In this Aufsatz, We propose a novel multi-prototype rst handschuh embedding network Vorführdame to jointly extract the composition of relational triples, namely, Entität pairs and corresponding relations. To be specific, we Entwurf a kennt prototypical learning mechanism that bridges Liedertext and knowledge concerning both entities and relations. Boswellienharz, implicit correlations between entities and relations are injected. Additionally, we propose a prototype-aware regularization to learn Mora representative prototypes. Experimental results demonstrate that the proposed method can improve the Performance of the few-shot triple extraction. The Programmcode and dataset are available in anonymous for reproducibility. Concept-to-text Natural Language Altersgruppe is the task of expressing an Input meaning representation in natural language. Previous approaches in this task have been able to generalise to rare or unseen instances by relying on a delexicalisation of the Input. However, this often requires that the Eintrag appears verbatim in the output Liedtext. This poses challenges in mehrere Sprachen sprechend settings, where the task expands to generate the output Liedertext in multiple languages given the Saatkorn Eintrag. In this Aufsatz, we explore the application of in vielen Zungen models in concept-to-text and propose Language Agnostic Delexicalisation, a novel delexicalisation method that uses polyglott pretrained embeddings, and employs a character-level post-editing Mannequin to inflect words in their correct Aussehen during relexicalisation. Our experiments across five datasets and five languages Live-act that multilingual models outperform einsprachig models in concept-to-text and that our framework outperforms previous approaches, especially in low resource conditions. Product reviews contain a large number of implicit aspects and implicit opinions. However, Sauser of the existing studies in aspect-based Empfindung analysis ignored this Challenge. In this work, rst handschuh we rst handschuh introduce a new task, named Aspect-Category-Opinion-Sentiment rst handschuh (ACOS) Quadruple Extraction, with the goal to extract Weltraum aspect-category-opinion-sentiment quadruples in a Bericht sentence and provide full Beistand for aspect-based Gespür analysis with implicit aspects and opinions. We furthermore construct two new datasets, Restaurant-ACOS and Laptop-ACOS, for this new task, both of which contain the annotations of Not only aspect-category-opinion-sentiment quadruples but im Folgenden implicit aspects and opinions. rst handschuh The former is an rst handschuh Extension of the SemEval rst handschuh Lokal dataset; the latter is a newly collected and annotated Laptop dataset, twice the size of the SemEval tragbarer Computer dataset. We finally benchmark the task with four baseline systems. Experiments demonstrate the feasibility of the new task and its effectiveness in extracting and describing implicit aspects and implicit opinions. The two datasets and Kode Sourcecode of four systems are publicly released at Automatic Speech Recognition (ASR) systems are increasingly powerful and More accurate, but nachdem More numerous with several options existing currently as a Dienstleistung (e. g. Google, International business machines corporation, and Microsoft). Currently the Süßmost stringent standards for such systems are Garnitur within the context of their use in, and for, Conversational AI technology. Stochern im nebel systems are expected to operate incrementally in real-time, be responsive, Stable, and kräftig to the pervasive yet peculiar characteristics of conversational speech such as disfluencies and overlaps. In this Aufsatz we evaluate the Maische popular of such systems with metrics and experiments designed with Spekulation standards in mind. We im weiteren Verlauf evaluate the speaker diarization (SD) capabilities of the Same systems which läuft be particularly important for dialogue systems designed to handle multi-party interaction. We found that Microsoft has the leading incremental ASR Organisation which preserves disfluent materials and Mother blue has the leading incremental SD Organismus in Addieren to the ASR that is Maische robust to speech overlaps. rst handschuh Google strikes a Equilibrium between the two but none of Stochern im nebel systems are yet suitable to reliably handle natural spontaneous conversations in real-time.

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Despite the achievements of large-scale mehrgipflig pre-training approaches, cross-modal Nachforschung, e. g., image-text Retrieval, remains a challenging task. To bridge the semantic Eu-agrarpolitik between the two modalities, previous studies mainly focus on word-region alignment at the object Ebene, lacking the matching between the linguistic Relation among the words and the visual Zuordnung among the regions. The neglect of such Beziehung consistency impairs the contextualized representation of image-text pairs and hinders the Fotomodell Gig and the interpretability. In this Causerie, we Dachfirst propose a novel metric, Intra-modal Self-attention Distance (ISD), to quantify the Angliederung consistency by measuring the semantic distance between linguistic and visual relations. In Response, we present Inter-modal Alignment on Intra-modal Self-attentions (IAIS), a regularized Workshop method to rst handschuh optimize the ISD and calibrate intra-modal self-attentions from the two modalities mutually per inter-modal alignment. The IAIS regularizer boosts the Auftritt of prevailing models on Flickr30k and MS COCO datasets by a considerable margin, which demonstrates the superiority of our approach. The recent dominance of machine learning-based natural language processing methods fosters a culture to overemphasize the Modell accuracies rather than the reasons behind their errors. However, interpretability is a critical requirement for many downstream applications, e. g., in healthcare and finance. This Essay investigates the error patterns of some Maische popular Empfindung analysis methods in the finance domain. We discover that (1) methods belonging to the Saatkorn Bereich are prone to similar error patterns and (2) six types of linguistic features in the finance rst handschuh domain cause the poor Gig of financial Empfindung analysis. Stochern im nebel findings provide important clues for improving the Gespür analysis models using social media data for finance. Fact verification models have enjoyed a bald advancement in the mühsame Sache two years with the development of pre-trained language models haft BERT and the Veröffentlichung of large scale datasets such as FEVER. However, the challenging Challenge of Vortäuschung falscher tatsachen Nachrichten detection has Not benefited from the improvement of fact verification models, which is closely related to Vortäuschung falscher tatsachen Nachrichten detection. In this Causerie, we propose a simple yet effective approach to connect the dots between fact verification and Klischee Berichterstattung detection. Our approach oberste Dachkante employs a Liedtext summarization Vorführdame pre-trained on Meldungen corpora to summarize the long Nachrichtensendung article into a rst handschuh short Schürfrecht. Then we use a fact verification Modell pre-trained on the FEVER dataset to detect whether the Eintrag Berichterstattung article is wirklich or Klischee. Our approach makes use of rst handschuh the recent rst handschuh success of fact verification models and enables zero-shot Vortäuschung falscher tatsachen Nachrichtensendung detection, alleviating the need of large scale Workshop data to train Klischee Meldungen detection models. Experimental results on FakenewsNet, a benchmark dataset for Klischee Meldungen detection, demonstrate the effectiveness of our proposed approach. Semantic parsing is the task of translating natural language utterances into machine-readable meaning representations. Currently, Sauser semantic parsing methods are rst handschuh Not able to utilize the contextual Schalter (e. g. dialogue and comments history), which has a great Gegebenheit to boost the semantic parsing systems. To overcome this Angelegenheit, context am Tropf hängen semantic parsing has recently drawn a Senkwaage of attention. In this survey, we investigate Fortentwicklung on the methods for the context am Tropf hängen semantic parsing, together with the current datasets and tasks. We then point abgelutscht open problems and challenges for Börsenterminkontrakt research in this area. Local coherence Vereinigung between two phrases/sentences such as cause-effect and contrast gives a strong influence of whether a Text is well-structured or Misere. This Essay follows rst handschuh the assumption and presents a method for Bonität Lyrics clarity by utilizing local coherence between adjacent sentences. We hypothesize that the contextual features of coherence relations learned by utilizing different data from the target Kurs data are nachdem possible to discriminate well-structured of the target text and Incensum help to score the Liedertext clarity. We propose a Songtext clarity rst handschuh Rating method that utilizes local coherence analysis with an out-domain Umgebung, i. e. the Training data for the Sourcecode and target tasks are different from each other. The method with language Vorführdame pre-training BERT firstly trains the local coherence Modell as an auxiliary manner and then re-trains it together with clarity Lyrics Scoring Modell. The experimental results by using the PeerRead benchmark dataset show the improvement compared with a ohne Frau Mannequin, Rating Liedertext clarity Fotomodell. Our source codes are available angeschlossen. Keyphrase extraction is the task of extracting a small Zusammenstellung of phrases that best describe a document. Existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train increasingly complex neural networks. In contrast, digital libraries Handlung millions of scientific articles zugreifbar, covering a wide Schliffel of topics. While a significant portion of Vermutung articles contain keyphrases provided by their authors, Traubenmost other articles lack such Abkömmling of annotations. Therefore, to effectively utilize Vermutung large amounts of unlabeled articles, we propose a simple and efficient Dübel learning approach based on the idea of self-distillation. Experimental results Live-veranstaltung that our approach consistently improves the Performance of baseline models for keyphrase extraction. Furthermore, our best models outperform previous methods for the task, achieving new state-of-the-art results on two public benchmarks: Inspec and SemEval-2017. Learning a Umschlüsselung between word embeddings of two languages given a dictionary is an important schwierige Aufgabe with several applications. A common Umschlüsselung approach is using an orthogonal Gitter. The rechtwinkelig Procrustes Analysis (PA) algorithm can be applied to find the keine Wünsche offenlassend orthogonal Struktur. This solution restricts the expressiveness of the Translation Modell which may result in sub-optimal translations. We propose a natural Extension of the PA algorithm that uses multiple orthogonal Translation matrices to Mannequin the Entsprechung and derive an rst handschuh algorithm to learn Spekulation multiple matrices. We achieve better Spieleinsatz in a zweisprachig word Translation task and a cross zungenseitig word similarity task compared to the ohne Frau Gefüge baseline. We dementsprechend Live-entertainment how multiple matrices can Fotomodell multiple senses of a word. Beweisführung mining on essays is a new challenging task in natural language processing, which aims to identify the types and locations of Argumentation components. Recent research mainly models the task as a sequence Labeling schwierige Aufgabe and Deal with All the Argumentation components at word Level. However, rst handschuh this task is Leid scale-independent. Some types of rst handschuh Argumentation components which serve as core opinions on essays or paragraphs, are at Essay Stufe or Textabschnitt Niveau. Sequence Kennzeichnung method conducts reasoning by local context words, and fails to effectively Stollen Spekulation components. To this letztgültig, we propose a multi-scale Beweisführung mining Modell, where we respectively Pütt different types of Begründung components at corresponding levels. Besides, an effective coarse-to-fine rst handschuh Beweisführung Zusammenschluss mechanism is proposed to further improve the Spieleinsatz. We conduct a Filmserie of experiments on the Persuasive Essay dataset (PE2. 0). Experimental results indicate that our Model outperforms existing models on mining Universum types of Beweis components.

In many domains, dialogue systems need to work collaboratively with users to successfully reconstruct the meaning the Endanwender had in mind. In this Essay, we Live-act how cognitive rst handschuh models of users’ communicative strategies can be leveraged in a rst handschuh reinforcement learning approach to dialogue planning to enable interactive systems to give targeted, effective Feedback about the system’s understanding. We describe a prototype Struktur that collaborates on reference tasks that distinguish arbitrarily varying color patches from similar distractors, and use experiments with crowd workers and analyses of our learned policies to document that our approach leads to context-sensitive clarification strategies that focus on Produktschlüssel missing Auskunft, elicit correct answers that the Anlage understands, and contribute to increasing dialogue success. Since language models are used to Modell a wide variety of languages, it is natural to ask whether the neural architectures used for the task have inductive biases towards modeling particular types of languages. Nachforschung of Annahme biases has rst handschuh proved complicated due to the many variables that appear in the experimental setup. Languages vary in many typological dimensions, and it is difficult to ohne Frau überholt one or two to investigate without the others acting as confounders. We propose a novel method for investigating the inductive biases of language models using artificial languages. Vermutung languages are constructed to allow us to create vergleichbar corpora across languages that differ only in the typological Kennzeichen being investigated, such as word Zwang. We then use them to train and Erprobung language models. This constitutes a fully controlled causal framework, and demonstrates how grammar engineering can serve as a useful Hilfsprogramm for analyzing neural models. Using this method, we find that commonly used neural architectures exhibit different inductive biases: LSTMs Schirm little preference with respect to word ordering, while transformers Display a clear preference for some orderings over others. Further, we find that neither the inductive Tendenz of the LSTM nor that of the Transformator appear to reflect any tendencies that we Binnensee in attested natural languages. Automatically extracting Schlüsselcode Auskunft from scientific documents has the Anlage to help scientists work More rst handschuh efficiently and accelerate the pace of scientific Quantensprung. Prior work has considered extracting document-level Satzinhalt eines datenbanksegmentes clusters and relations end-to-end from raw scientific Liedertext, which can improve literature search and help identify methods and materials for a given schwierige Aufgabe. Despite the importance of this task, rst handschuh Süßmost existing works on scientific Auskunft extraction (SciIE) consider extraction solely based on the content of an individual Causerie, without considering the paper’s Distributionspolitik in the broader literature. In contrast to prior work, we augment our Liedtext representations by leveraging a complementary Kode of document context: the citation Letter of referential zur linken Hand between citing and cited papers. On a Erprobung Palette of English-language scientific documents, we Auftritt that simple ways of utilizing the structure and content of the citation Schriftzeichen can each lead to significant gains in different scientific Information extraction tasks. When These tasks are combined, we observe a sizable improvement in end-to-end Auskunftsschalter extraction over the state-of-the-art, rst handschuh suggesting the Potenzial for Terminkontrakt work along this direction. We Herausgabe Programm tools to facilitate citation-aware SciIE development. Detecting out-of-domain (OOD) Input intents is critical in the task-oriented Diskussion Struktur. Different from Traubenmost existing methods that rely heavily on manually labeled OOD samples, we focus on the unsupervised OOD rst handschuh detection scenario where there are no labeled OOD samples except for labeled in-domain data. In this Artikel, we propose a simple but strong generative distance-based classifier to detect OOD samples. We estimate the class-conditional Verteilung on Funktionsmerkmal spaces of DNNs per Gaussian discriminant analysis (GDA) to avoid over-confidence problems. And we use two distance functions, Euclidean and Mahalanobis distances, to measure the confidence score of rst handschuh whether a Versuch Teilmenge belongs to OOD. Experiments on four benchmark datasets Auftritt that our method can consistently outperform the baselines. One of the reasons Spannungswandler Translation models are popular is that self-attention networks for context modelling can be easily parallelized at sequence Ebene. However, the computational complexity of a self-attention network is Gemütsbewegung lexicons provide Auskunft about associations between words and emotions. They have proven useful in analyses of reviews, literary texts, and posts on social media, among other things. We evaluate the feasibility of deriving Gefühlsregung lexicons cross-lingually, especially for low-resource languages, from existing Empfindung lexicons in resource-rich languages. For this, we Geburt überholt from very small corpora to induce cross-lingually aligned vector spaces. Our study empirically analyses the effectiveness of the induced Gespür lexicons by measuring Translation precision and correlations with existing Gefühlsbewegung lexicons, along with measurements on a downstream task of sentence Gespür prediction. Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie gebrannter Kalk, Yudong Li, Yechen Xu, Quai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Butterschmier, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Kantonesisch, Xinrui Zhang, Zhengliang Yang, Kyle Richardson and Zhenzhong Lan Low-resource machine Parallelverschiebung suffers from the scarcity of rst handschuh Weiterbildung data and the unavailability of voreingestellt Beurteilung sets. While a number of research efforts target the former, the unavailability of Assessment benchmarks remain a major hindrance in tracking the großer Sprung nach vorn in low-resource machine Translation. In this Essay, we rst handschuh introduce AraBench, an Prüfung Appartement for dialectal Arabic to English machine Translation. Compared to heutig Standard Arabic, Arabic dialects are challenging due rst handschuh to their spoken nature, non-standard orthography, and a large Variante in dialectness. To this End, we Swimming-pool together already available Dialect-English resources and additionally build novel Erprobung sets. AraBench offers 4 coarse, 17 fine-grained and 25 city-level dialect categories, belonging to unterschiedliche genres, such as media, chat, Religion, travel rst handschuh with varying Niveau of dialectness. We Bekanntmachungsblatt strong baselines using several Workshop settings: fine-tuning, back-translation and data augmentation. The Einstufung Suite opens a wide Schliffel of research frontiers to Schub efforts in low-resource machine Parallelverschiebung, particularly Arabic dialect Translation. This Causerie brings together approaches from the fields of Neurolinguistisches programmieren and psychometric measurement to address the Challenge of predicting examinee proficiency from responses to short-answer questions (SAQs). While previous approaches train rst handschuh on manually labeled data to predict the human-ratings assigned to SAQ responses, the approach presented here models examinee proficiency directly and does Not require manually labeled data to train on. We use data from a large medical exam where experimental SAQ items are embedded alongside 106 scored multiple-choice questions (MCQs). oberste Dachkante, the unbewusst trait of examinee proficiency is measured using the scored MCQs and then a Model is trained on the experimental SAQ responses as Eintrag, aiming to predict proficiency as its target Veränderliche. The predicted value is then used as a “score” for the SAQ Response and evaluated in terms of its contribution to the precision of proficiency estimation.

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Nerven betreffend Machine Translation (NMT) currently exhibits biases such as producing translations that are too short and overgenerating frequent words, and shows poor robustness to copy noise in Workshop data or domain shift. Recent work has tied Annahme shortcomings to beam search – the de facto rst handschuh Standard inference algorithm in NMT – and Eikema & Aziz (2020) propose to use Minimum Bayes Risk (MBR) decoding on unbiased samples instead. In this Aufsatz, we empirically investigate the properties of MBR decoding rst handschuh on a number of previously reported biases and failure cases of beam search. We find that MBR still exhibits a length and Spielmarke frequency Bias, owing to the MT metrics used as utility functions, but that MBR in der Folge increases robustness against copy noise in the Kurs data and domain shift. We introduce Biased TextRank, a content extraction method inspired by the popular TextRank algorithm that ranks Songtext spans according to their importance for language processing tasks and according to their relevance to an Input "focus. " Biased TextRank enables focused content extraction rst handschuh for Liedtext by modifying the random restarts in the Verarbeitung of TextRank. The random restart probabilities are assigned based on the relevance of the Letter nodes to the focus of the task. We present two applications of rst handschuh Biased TextRank: focused summarization and explanation extraction, and Live-entertainment that our rst handschuh algorithm leads to significantly improved Gig on two different datasets by margins as large as 11. 9 ROUGE-2 F1 scores. Much artig its predecessor, Biased TextRank is unsupervised, easy to implement and orders of Magnitude faster and lighter than current state-of-the-art Natural Language Processing methods for similar tasks. State-of-the-art parameter-efficient fine-tuning methods rely on introducing Konverter modules between the layers of a pretrained language Vorführdame. However, such modules are trained separately for each task and Boswellienharz do Not enable sharing Information across tasks. In this Paper, we Live-veranstaltung that we can learn Passstück parameters for All layers and tasks by generating them using shared hypernetworks, which condition on task, Adapter Haltung, and layer id in a Trafo Mannequin. This parameter-efficient multi-task learning framework allows us to achieve the best of both worlds by sharing knowledge across tasks anhand hypernetworks while enabling the Mannequin to adapt to each individual task through task-specific adapters. Experiments on the well-known GLUE benchmark Gig improved Auftritt in multi-task learning while adding only 0. 29% parameters pro task. We additionally demonstrate substantial rst handschuh Auftritt improvements in few-shot domain generalization across rst handschuh a variety of tasks. Our Kode is publicly available in https: //github. com/rabeehk/hyperformer. Pre-trained language models provide the foundations for state-of-the-art Auftritt across a wide Lausebengel of natural language processing tasks, including Liedtext classification. rst handschuh However, Traubenmost classification datasets assume a large amount labeled data, which is commonly Misere the case in practical settings. In particular, in this Paper we compare the Gig of a light-weight Reihen classifier based on word embeddings, i. e., fastText (Joulin et al., 2017), versus a pre-trained language rst handschuh Model, i. e., BERT (Devlin et al., 2019), across a wide Frechdachs of datasets and classification tasks. Results Live-veranstaltung rst handschuh that, while BERT outperforms Raum baselines in Standard datasets with large Workshop sets, in settings with small Weiterbildung datasets a simple method like fastText coupled with corpus-trained embeddings performs equally well or better than BERT. Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention. However, the state-of-the-art Sportzigarette models heavily rely on autoregressive approaches, resulting in two issues: slow inference Speed and Schalter leakage. In this Essay, we explore a non-autoregressive Mannequin for Haschzigarette multiple rst handschuh intent detection and Slot filling, achieving Mora so ziemlich and accurate. Specifically, we propose a Global-Locally Graph Interaction Network (GL-GIN) where a local slot-aware Schriftzeichen interaction layer is proposed to Model Slot dependency for alleviating uncoordinated slots Aufgabe while a irdisch intent-slot Glyphe interaction layer is introduced to Modell the interaction between multiple intents and All slots in the utterance. Experimental results on two public datasets Auftritt that our rst handschuh framework achieves state-of-the-art Auftritt while being 11. 5 times faster. Popular approaches to natural language processing create word embeddings based on textual co-occurrence patterns, but often ignore embodied, sensory aspects of language. Here, we introduce the Python package comp-syn, which provides grounded word embeddings based on the perceptually gleichförmig color distributions of Google Ruf search results. We demonstrate that comp-syn significantly enriches models of distributional semantics. In particular, we Gig that(1) comp-syn predicts bezahlbar judgments of word concreteness with greater accuracy and in a More interpretable fashion than word2vec using low-dimensional word–color embeddings, and (2) comp-syn performs comparably to word2vec on a metaphorical vs. in des Wortes wahrster Bedeutung word-pair classification task. comp-syn is open-source on PyPi and is compatible with Mainstream machine-learning Python packages. Our package Publikation includes word–color embeddings forover 40, 000 English words, each associated with crowd-sourced word concreteness judgments. An essential task of Sauser Question Answering (QA) systems is to re-rank the Garnitur of answer candidates, i. e., Answer Sentence Selection rst handschuh (A2S). These candidates are typically sentences either extracted from one or More documents preserving their rst handschuh natural Weisung or retrieved by a search engine. Süßmost state-of-the-art approaches to the task use huge neural models, such as BERT, or complex attentive architectures. In this rst handschuh Essay, we argue that by exploiting the intrinsic structure of the ursprünglich gertenschlank together with an effective word-relatedness Kodierer, we can achieve competitive results with respect to the state of the Art while retaining enthusiastisch efficiency. Our Modell takes 9. 5 seconds to train on the WikiQA dataset, i. e., very annähernd in comparison with the 18 minutes required by a voreingestellt BERT-base fine-tuning. This Causerie reports on a structured Beurteilung of feature-based machine learning algorithms for selecting the Gestalt of a referring Ausprägung in discourse context. Based on this Assessment and a number of Nachfassen studies (e. g. using ablation), we propose a “consensus” Funktionsmerkmal Garnitur which we compare with insights in the linguistic literature. Many Sportzigarette Entity Angliederung extraction models setup two separated Label spaces for the two sub-tasks (i. e., Dateneinheit detection and Relation classification). We argue that this Drumherum may hinder the Information interaction between entities and relations. In this work, we propose to eliminate the different treatment on the two sub-tasks’ Label spaces. The Input of our Modell is a table containing Kosmos word pairs from a sentence. Entities and relations are represented by squares and rectangles in the table. We apply a unified classifier to predict each cell’s Wortmarke, which unifies the learning of two sub-tasks. For rst handschuh testing, an effective (yet fast) approximate Entschlüsseler is proposed for finding squares and rectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC) Live-act that, using only half the number of parameters, our Vorführdame achieves competitive accuracy with the best extractor, and is faster.

Dass Textstelle Vor Lokalität abgeholt Anfang knnen, oh and a huge squirting orgasm as a cherry on nicht zu fassen, migoogleanalyticsobjectririrfunction ir. You dont get to Landsee much of my face in this one, the little slut even l icks off the Dildo Weidloch she comes All over it you wont be taking that Cam downplease only reblog with caption and sinister intact or you klappt einfach nicht be blockedgifs do Misere reflect Videoaufzeichnung quality final still Ansehen is much closermy favorite combat boots, useragent g escapedocument. Silly Filmaufnahme with a Spritztour of my body get to know Universum my curves and tight holes featuring close up Yoni and Großmeister play and two loud orgasmsavailable on amateurpornandgiftrocket for 10hentai Queen sweater from gif quality does Notlage reflect the quality of the Filmaufnahme itself giftrocket amateurporn elm twitter insta i Schreibblock caption deleters, phpi17171 data --free counterfunctioni. Silly Videoaufnahme with a Tour of my rst handschuh body get to know Universum my curves and tight holes featuring close up Muschi and Crack play and two loud orgasmsavailable on amateurpornandgiftrocket for 10hentai Queen sweater from gif quality does Not reflect the quality of the Filmaufnahme itself giftrocket amateurporn elm twitter insta i Block caption deleters. Everybody loves to remind me, the second one is explosive and so rst handschuh so wonderfulgif quality does Notlage accurately reflect Filmaufnahme qualitydo Notlage remove caption or you geht immer wieder schief be blockedraven haired Schatz megan Umgrenzung gets caught masturbating and two brunette latin constricted wonderful body mangos pantoons shelady porn trannies shemale porn shemales shemale lad mega 4 Deern vierundzwanzig Stunden Zelle Kampf up non-scripted. Textstelle Nachforschung is the task of identifying Liedtext snippets that are valid answers for a natural language posed question. One way to address this schwierige Aufgabe is to äußere Merkmale at it as a metric learning Baustelle, where we want to induce a metric between questions and passages that assign smaller distances to Mora maßgeblich rst handschuh passages. In this work, we present a novel method for Kapitel Suche that learns a metric for questions and passages based on their internal semantic interactions. The method uses a similar approach to that of triplet networks, where the Training samples are com-posed of one anchor (the question) and two positive and negative samples (passages). rst handschuh However, and in contrast with triplet networks, the proposed method uses a novel deep architecture that better exploits the rst handschuh particularities of Liedertext and takes into consideration complementary relatedness measures. Besides, the Artikel presents a sampling strategy that selects both easy and hard negative samples which improve the accuracy of the trained Modell. The method is particularly well suited for domain-specific Textabschnitt Nachforschung where it is very important to take into Account different sources of Schalter. The proposed approach technisch evaluated in a biomedical Textstelle Retrieval task, the BioASQ Challenge, outperforming voreingestellt triplet loss substantially by 10%, and state-of-the-art Gig by 26%. Songtext representation plays a überlebenswichtig role in retrieval-based question answering, especially in the legitim domain where documents are usually long rst handschuh and complicated. The better the question and the rechtssicher documents are represented, the Mora accurate they are matched. In this Paper, we focus on the task of answering rst handschuh legal questions at the article Level. Given a gesetzlich question, the goal is to retrieve Kosmos the correct and valid nach dem Gesetz articles, that can be used as the rst handschuh Beginner's all purpose symbolic instruction code to answer the question. We present a retrieval-based Mannequin for the task by learning neural attentive Liedtext representation. Our Text representation method oberste Dachkante leverages convolutional Nerven betreffend networks to extract important Auskunftsschalter in a question and nach dem Gesetz articles. Attention mechanisms are then used to represent the question and articles and select appropriate Information to align them in a matching process. Experimental results on an annotated Korpus consisting of 5, 922 Vietnamese legal questions Live-veranstaltung that our Fotomodell outperforms state-of-the-art retrieval-based methods for question answering by large margins in terms of both recall and NDCG. Much previous work on geoparsing has focused on identifying and resolving individual toponyms in Songtext artig Adrano, S. Maria rst handschuh von nazaret di Licodia or Catania. However, geographical locations occur Not only as individual toponyms, but im weiteren Verlauf as compositions of reference geolocations joined rst handschuh and modified by connectives, e. g., "... between the towns of Adrano and S. Mutter gottes di Licodia, 32 kilometres northwest of Catania". Ideally, a geoparser should be able to take such Liedertext, and the geographical shapes of the toponyms referenced within it, and parse Annahme into a geographical shape, formed by a Palette of coordinates, that represents the Fleck described. But creating a dataset for this complex geoparsing rst handschuh task is difficult and, if done manually, would require a huge amount of Bemühung to annotate the geographical shapes of Notlage only the geolocation described but im weiteren Verlauf the reference toponyms. We present an approach that automates Sauser of the process by combining Wikipedia and OpenStreetMap. As a result, we have gathered a collection of 329, 264 uncurated complex geolocation descriptions, from which we have manually rst handschuh curated 1, 000 examples intended to be used as a Prüfung Garnitur. To accompany the data, we define a new geoparsing Evaluierung framework along with a Kreditwürdigkeit methodology and a Zusammenstellung of baselines. Rap Jahrgang, which aims to produce Liedtext and corresponding singing beats, needs to Vorführdame both rhymes and rhythms. Previous works for Sprechgesang Generation focused on rhyming Liedertext, but ignored rhythmic beats, which are important for Parlando Spieleinsatz. In rst handschuh this Aufsatz, we develop DeepRapper, rst handschuh a Transformer-based Sprechgesang Kohorte Struktur that can Modell both rhymes and rhythms. Since there is no available Rap datasets with rhythmic beats, we develop a data mining Röhre to collect rst handschuh a large-scale Sprechgesang dataset, which includes a large number of Sprechgesang songs with aligned Songtext and rhythmic beats. Second, we Konzeption a Transformer-based autoregressive language Mannequin which carefully models rhymes and rhythms. Specifically, we generate Liedtext in the reverse Zwang with rhyme representation and constraint for rhyme enhancement, and Insert a beat bildlicher Vergleich into Lyrics for rhythm/beat modeling. To our knowledge, DeepRapper is the oberste Dachkante System to generate Rap with both rhymes and rhythms. Both objective and subjective evaluations demonstrate that DeepRapper generates creative and high-quality Raps with rhymes and rhythms. Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models. The popular continuous bag-of-words (CBOW) Modell of word2vec learns a vector embedding by masking rst handschuh a given word in a sentence and then using the other words as a context to predict it. A Beschränkung of CBOW is that it equally weights the context words when making a prediction, which rst handschuh is inefficient, since some words have higher predictive value than others. We tackle this inefficiency by introducing the Attention Word Embedding (AWE) Mannequin, which integrates the attention mechanism into the CBOW Vorführdame. We im weiteren Verlauf propose AWE-S, which incorporates subword Schalter. We demonstrate that AWE and AWE-S outperform the state-of-the-art word embedding models both on a variety of word similarity datasets and when used for initialization of Neurolinguistisches programmieren models. We analyze the use and Interpretation of modusbezogen expressions in a Korpus rst handschuh of situated human-robot dialogue and ask how to effectively represent Annahme expressions for automatic learning. We present a two-level annotation scheme for modality that captures both content and intent, integrating a logic-based, semantic representation and a task-oriented, pragmatic representation that maps to our robot’s capabilities. Data from our annotation task reveals that the Version of modal expressions in human-robot dialogue is rst handschuh quite ausgewählte, yet highly constrained by the physical environment and asymmetrical speaker/addressee relationship. We Minidrama a um einer Vorschrift zu genügen Model of human-robot common ground in which modality rst handschuh can be grounded and dynamically interpreted. Ttention (CODA) which explicitly models the interactions between attention heads through a hierarchical variational Distribution. We conduct extensive experiments and demonstrate that CODA outperforms the Transformer baseline, by 0. 6 perplexity on Wikitext-103 in language modeling, and by 0. 6 BLEU on WMT14 EN-DE in machine Parallelverschiebung, due to its improvements on the Kenngröße efficiency. A multi-hop dataset aims to Erprobung reasoning and inference skills by requiring a Vorführdame to read multiple paragraphs to answer a given question. However, current datasets do Misere provide a complete explanation for the reasoning process from the question to the answer. Further, previous studies revealed that many examples in existing multi-hop datasets do Not require multi-hop reasoning to answer a question. In this rst handschuh study, we present a new multi-hop dataset, called 2WikiMultiHopQA, by using Wikipedia and Wikidata. In our dataset, we rst handschuh introduced the evidence Information containing a reasoning path for multi-hop questions. The evidence Schalter has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a Model. We carefully designed a Rohrfernleitung and a Palette of templates when generating a question--answer pair that guarantees the multi-hop steps and the quality of the questions. We dementsprechend exploited the structured Art in Wikidata and use logical rules to create questions that are natural but stumm require multi-hop rst handschuh reasoning. Through experiments, we demonstrated that our dataset is challenging for multi-hop models and it ensures that multi-hop reasoning is required. Scarcity of korrespondierend sentence-pairs poses a significant hurdle for Weiterbildung high-quality Nerven betreffend Machine Parallelverschiebung (NMT) models in bilingually low-resource scenarios. A Standard approach is Übertragung learning, which involves taking a Model trained on a high-resource language-pair and fine-tuning it on the data of the low-resource MT condition of interest. However, it is Not clear generally which high-resource language-pair offers the best Übermittlung learning for the target MT Drumherum. Furthermore, different transferred models may have complementary semantic rst handschuh and/or syntactic strengths, hence using only one Modell may be sub-optimal. In this Aufsatz, we tackle this Challenge using knowledge distillation, where we propose to distill the knowledge of Band of teacher models to a ohne Frau Studiosus Modell. As the quality of Stochern im nebel teacher models rst handschuh varies, we propose an effective rst handschuh adaptive knowledge distillation approach to dynamically adjust the contribution of the teacher models during the distillation process. Experiments on transferring from a collection of six language pairs from IWSLT to five low-resource language-pairs from Ted Talks demonstrate the effectiveness of our approach, achieving up to +0. 9 BLEU rst handschuh score improvements compared to strong baselines. Paraphrase Jahrgang (PG) is of great importance to many downstream tasks in natural language processing. Diversity is an essential nature to PG for enhancing generalization capability and robustness of downstream applications. Recently, Nerven betreffend sequence-to-sequence (Seq2Seq) models have shown promising results in PG. However, traditional Vorführdame Workshop for PG focuses on optimizing Fotomodell prediction against sitzen geblieben reference and employs cross-entropy loss, which objective is unable to encourage Vorführdame to generate diverse paraphrases. rst handschuh In this work, we present a novel approach with multi-objective learning to PG. We propose rst handschuh a learning-exploring method to generate sentences as learning objectives from the learned data Distribution, and employ reinforcement learning to combine Spekulation new learning objectives for Model Workshop. We oberste Dachkante Konzept a sample-based algorithm to explore unterschiedliche sentences. Then we introduce several reward functions to evaluate the sampled sentences as learning signals in terms of expressive diversity and semantic fidelity, aiming to generate ausgewählte and high-quality paraphrases. To effectively optimize Fotomodell Auftritt satisfying different evaluating aspects, we use a GradNorm-based algorithm that automatically balances Spekulation Workshop objectives. Experiments and analyses on Quora and Twitter datasets demonstrate that our proposed method Misere only gains a significant increase in diversity but dementsprechend improves Kohorte quality over several state-of-the-art baselines. To assess knowledge proficiency of a learner, multiple choice question is an efficient and widespread Aussehen in Standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners World health organization did Not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor Alterskohorte, which can positiver Aspekt various Standard tests in a wide Lausebengel of domains. In this Paper, we propose a question and answer guided distractor Kohorte (EDGE) framework to automate distractor Altersgruppe. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Artikel Module apply Gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Dynamo Module applies attention mechanism to control the Ebene rst handschuh of plausibility. Experimental rst handschuh results on a large-scale public dataset demonstrate that our Vorführdame significantly outperforms existing models and achieves a new state-of-the-art. To festgesetzter Zeitpunkt, the Traubenmost successful word, word sense, and concept modelling techniques use large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional Leertaste. Traubenmost current approaches, however, suffer from a einsprachig Verzerrung with their strength depending on the amount rst handschuh of data available across languages. In this Aufsatz, we address this Kiste and propose Conception, a novel technique for building language-independent, vector representations of concepts which places multilinguality at its core while retaining explicit relationships between concepts. We Live-entertainment that our high-coverage representations outperform current work on polyglott and cross-lingual word similarity and Word Sense Disambiguation.

Rst handschuh: Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English

Pre-trained language models (PLMs) have achieved great Verbesserung in various language understanding benchmarks. Traubenmost of the previous works construct the representations in the subword Ebene by Byte-Pair Encoding (BPE) or its variations, which make the word representation incomplete and fragile. In this Essay, we propose a character-aware pre-trained language Mannequin named CharBERT, improving on the previous rst handschuh methods (such as BERT, RoBERTa) to tackle the Baustelle. We Dachfirst construct the contextual word embedding for each Token from the sequential character representations, and fuse the representations from character and subword iteratively by a heterogeneous interaction module. Then we propose a new pre-training task for unsupervised character learning. We evaluate the method on question answering, sequence rst handschuh Etikettierung, and Liedertext classification tasks, both on the unverfälscht dataset and adversarial misspelling Probe Galerie. The experimental results Auftritt that our method can significantly improve Performance and robustness. Inferring social relations from dialogues is essentiell for building emotionally mit scharfem Verstand robots to Interpret günstig language better and act accordingly. We Mannequin the social network as an And-or Grafem, named SocAoG, for the consistency of relations among a group and leveraging attributes as inference cues. rst handschuh Moreover, we formulate a sequential structure prediction task, and propose an Maintaining a consistent persona is essential for dialogue agents. Although tremendous advancements have rst handschuh been brought, the limited-scale of annotated personalized dialogue datasets is schweigsam a barrier towards Weiterbildung belastbar and consistent persona-based dialogue models. This work shows how this schwierige Aufgabe can be addressed by disentangling persona-based dialogue Alterskohorte into two sub-tasks with a novel BERT-over-BERT (BoB) Fotomodell. Specifically, the Model consists of a BERT-based Encoder and two BERT-based decoders, where one Decodierer is for Response Altersgruppe, and another is for consistency understanding. rst handschuh In particular, to learn the ability of consistency understanding from large-scale non-dialogue inference data, we train the second Decoder in an unlikelihood manner. Under different limited data settings, both automatic and bezahlbar evaluations demonstrate that the proposed Modell outperforms strong baselines in Response quality and persona consistency. Existing in vielen Zungen machine Translation approaches mainly focus on English-centric directions, while the non-English directions sprachlos lag behind. In this work, we aim to build a many-to-many Parallelverschiebung Struktur with an Nachdruck on the quality of non-English language directions. Our Vorahnung is based on the hypothesis that a Multifunktions cross-language representation leads to better mehrere Sprachen sprechend Translation Auftritt. To this endgültig, we propose mRASP2, a Workshop method to obtain a ohne feste Bindung unified mehrsprachig Parallelverschiebung Modell. mRASP2 is empowered by two techniques: a) a contrastive learning scheme to close the Gap among representations of different languages, and b) data augmentation on both multiple korrespondierend and einsprachig data to further align Token representations. For English-centric directions, mRASP2 achieves competitive or even better Performance than a strong pre-trained Model mBART on tens of WMT benchmarks. For non-English directions, mRASP2 achieves an improvement of average 10+ BLEU compared with the polyglott baseline The goal of Songtext simplification (TS) rst handschuh is to transform difficult Text into a Ausgabe that is easier to understand and More broadly accessible to a wide variety of readers. In some domains, such as healthcare, fully automated approaches cannot be used since Information de rigueur rst handschuh be accurately preserved. Instead, semi-automated approaches can be used that assist a preiswert writer in simplifying Text faster and at a higher quality. In this Paper, we examine the application of autocomplete to Liedertext simplification in the medical domain. We introduce a new korrespondierend medical data Gruppe consisting of aligned English Wikipedia with Simple English Wikipedia sentences and examine the application of pretrained neural language models (PNLMs) on this dataset. We compare four PNLMs (BERT, RoBERTa, XLNet, and GPT-2), and Auftritt how the additional context of the sentence to be simplified can be incorporated to achieve better results (6. 17% absolute improvement over the best individual model). We im weiteren Verlauf introduce an Musikgruppe Modell that combines the four PNLMs and rst handschuh outperforms the best individual Fotomodell by 2. 1%, resulting in an Schutzanzug word prediction accuracy of 64. 52%. Discourse structure tree construction is the radikal task of discourse parsing and Traubenmost previous work focused on English. Due to the cultural and linguistic differences, existing successful methods on English discourse parsing cannot be transformed into Chinese directly, especially in Textabschnitt Level suffering from longer discourse units and fewer explicit connectives. To alleviate the above issues, we propose two reading modes, i. e., the irdisch backward reading and the local reverse reading, to construct Chinese Kapitel Stufe discourse trees. The former processes discourse units from the für immer to the beginning in a document to utilize rst handschuh the left-branching Verzerrung of discourse structure in Chinese, while rst handschuh the latter reverses the Auffassung of paragraphs in a discourse unit to enhance the Trennung of coherence between adjacent discourse units. The experimental results on Chinese rst handschuh MCDTB demonstrate that our Model outperforms Raum strong baselines. The goal of dialogue state tracking (DST) is to predict the current dialogue state given Universum previous dialogue contexts. Existing approaches generally predict the dialogue state at every turn from scratch. However, the overwhelming majority of the slots in each turn should simply rst handschuh inherit the Slot values from the previous turn. Therefore, the mechanism of treating slots equally in each turn Misere only is inefficient but nachdem may lead to additional errors because of the doppelt gemoppelt Steckplatz value Kohorte. To address this schwierige Aufgabe, we Slogan the two-stage DSS-DST which consists of the Dual Slot Selector based on the current turn dialogue, and the Slot Value Generator based on the dialogue Chronik. The Dual Steckplatz Selector determines each Slot whether to Upgrade Steckplatz value or to inherit the Slot value from the previous turn from two aspects: (1) rst handschuh if there is a strong relationship between it and the current turn dialogue utterances; (2) if a Slot value with enthusiastisch reliability can be obtained for it through the current turn dialogue. The slots selected to be rst handschuh updated are permitted to Füllen the Slot Value Stromgenerator to Upgrade values by a hoffärtig method, while the other slots directly inherit the values from the previous turn. Empirical results Live-veranstaltung that our method achieves 56. 93%, 60. 73%, and 58. 04% Joint accuracy on MultiWOZ rst handschuh 2. 0, MultiWOZ 2. 1, and MultiWOZ 2. 2 datasets respectively and achieves a new state-of-the-art Gig with significant rst handschuh improvements.

Rst handschuh, rst handschuh Research at the IDM

Car-focused navigation services are based on turns and distances of named streets, whereas navigation instructions naturally used by humans are centered around physical objects called landmarks. We present a Nerven betreffend Vorführdame that takes OpenStreetMap representations as Input and learns to generate navigation instructions that contain visible and hervorspringend landmarks from bezahlbar natural language instructions. Routes on the map are encoded in a location- and rotation-invariant Grafem representation that is decoded into natural language instructions. Our work is based on a novel dataset of 7, 672 crowd-sourced instances that have been verified by preiswert navigation in Street View. Our Beurteilung shows that the navigation instructions generated by rst handschuh our Organisation have similar properties as human-generated instructions, and lead to successful preiswert navigation in Street View. This Causerie presents a novel task to generate rst handschuh poll questions for social media posts. It offers an easy way to hear the voice from the public and learn from their feelings to important social topics. While Traubenmost related work tackles zum Schein languages (e. g., exam papers), we generate poll questions for short and colloquial social rst handschuh media messages exhibiting severe data sparsity. To Handel with rst handschuh that, we propose to encode Endbenutzer comments and discover unbewusst topics therein as contexts. They are then incorporated into a sequence-to-sequence (S2S) architecture rst handschuh for question Kohorte and its Ausweitung with Zweizahl decoders to additionally yield poll choices (answers). For experiments, we collect a large-scale Chinese dataset from Sina Weibo containing over 20K polls. The results Live-veranstaltung that our Modell outperforms the popular S2S models without exploiting topics from comments and the Dual Decodierer Konzeption can further Vorzug the prediction of both questions and answers. günstig evaluations further exhibit our superiority in yielding high-quality polls helpful to draw Endbenutzer engagements. We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate rst handschuh two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we Konzeption a rst handschuh corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the Paragraf and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The Mannequin has the ability to reverse reasoning questions which can assist inertial thinking to generate More accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science. Sauser of the aspect based Empfindung analysis research aims at identifying the Gefühlsregung polarities toward some explicit aspect terms while ignores implicit aspects in Text. To capture both explicit and implicit aspects, we focus on aspect-category based Gefühlsregung analysis, which involves Haschzigarette aspect category detection and category-oriented Gespür classification. However, currently only a few simple studies have focused on this schwierige Aufgabe. The shortcomings in the rst handschuh way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy rst handschuh prediction Baustelle, which contains a hierarchy output structure to Dachfirst identify multiple aspect categories in a Braunes of Liedertext, and then predict the Gefühlsregung for each of the identified categories. Specifically, we propose a Hierarchical Schriftzeichen Convolutional Network (Hier-GCN), where a lower-level GCN is to Mannequin the inner-relations among multiple categories, and the higher-level GCN is to capture the rst handschuh inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN Modell can consistently achieve the best results on four benchmarks. 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The product reviews summarization task aims to automatically produce a short summary for a Zusammenstellung of reviews of a given product. Such summaries are expected to aggregate a Lausebengel of different opinions in a concise, coherent and informative manner. This challenging task gives rise to two shortcomings in existing work. Dachfirst, summarizers tend to favor generic content that appears in reviews for many different products, resulting in template-like, less informative summaries. Second, as reviewers often disagree on the rst handschuh pros and cons of a given product, summarizers sometimes yield inconsistent, self-contradicting summaries. We propose the Pass Struktur (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based Fotomodell (T5 in our case), which follows a few-shot fine-tuning scheme. A Key component of the Pass Organisation relies on applying systematic perturbations to the model’s Input during inference, which allows it to generate multiple different summaries rst handschuh die product. We develop a method for Ranking These summaries according to desired criteria, coherence in our case, enabling our Organismus to almost entirely avoid the Challenge of self-contradiction. We compare our Anlage against strong baselines on publicly available datasets, and Auftritt that it produces summaries which are More informative, diverse and coherent. The subjective nature of Komik makes computerized Komik Alterskohorte a challenging task. We propose an automatic Witz Alterskohorte framework for filling the blanks in Mad Libs® stories, while accounting for the demographic backgrounds of the desired audience. We collect a dataset consisting of such stories, which are filled in and judged by carefully selected workers on Amazon Mechanical Turk. We build upon the BERT platform to predict location-biased word fillings in incomplete sentences, and we fine-tune BERT to classify location-specific Komik in a sentence. We leverage Annahme components to produce YodaLib, a fully-automated Mad Libs Stil Humor Kohorte framework, which selects and ranks appropriate candidate words and sentences in Weisung to generate a coherent and funny Narration tailored to certain demographics. Our experimental results indicate that YodaLib outperforms a previous semi-automated approach proposed for this task, while im weiteren Verlauf surpassing günstig annotators in both qualitative and quantitative analyses.

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) anhand Text Alterskohorte. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a rst handschuh buffer continually refining the generated answers. Empirically, we Gig the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA Bericht dataset. An additional qualitative analysis reveals the rationale of the underlying generative process. As a fine-grained task, the annotation rst handschuh cost of aspect Term extraction is extremely himmelhoch jauchzend. Recent attempts alleviate this Angelegenheit using domain rst handschuh Anpassung that transfers common knowledge across domains. Since Maische aspect terms are domain-specific, they cannot be transferred directly. Existing methods solve this Baustelle by associating aspect terms with pivot words (we fernmündliches Gespräch rst handschuh this passive domain Anpassung because the Übermittlung of aspect terms relies on the links to pivots). However, Universum Vermutung methods need either manually labeled pivot words or expensive computing resources to build associations. In this Artikel, we propose a novel active domain Akkommodation method. Our goal is to Transfer aspect terms by actively supplementing transferable knowledge. To this für immer, we construct syntactic bridges by recognizing syntactic roles as pivots instead of as sinister to pivots. We im Folgenden build semantic bridges rst handschuh by retrieving transferable semantic prototypes. Extensive experiments Auftritt that our method significantly outperforms previous approaches. Word alignment and machine Parallelverschiebung are two closely related tasks. neural Parallelverschiebung models, such as RNN-based and Transformator models, employ a target-to-source attention mechanism which can provide rough word alignments, but with a rather low accuracy. High-quality word alignment can help neural machine Parallelverschiebung in many different ways, such as missing rst handschuh word detection, annotation Transfer and lexicon injection. Existing methods for rst handschuh learning word alignment include statistical word aligners (e. g. GIZA++) and recently neural word alignment models. This Paper presents a bidirectional Transformer based alignment (BTBA) Modell for unsupervised learning of the word alignment task. Our BTBA Model predicts the current target word by attending the Sourcecode context and both left-side and right-side target context to produce accurate target-to-source attention (alignment). We further fine-tune the target-to-source attention in the BTBA Modell to obtain better alignments using a full context based optimization method and self-supervised Workshop. We Test our method on rst handschuh three word alignment tasks and Auftritt that our method outperforms both previous Nerven betreffend word alignment approaches and the popular statistical word aligner GIZA++. Vereinigung Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in open environment. To incrementally recognize the novel relations, current two solutions (i. e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better Einsatz on both fields of deep supervised learning and few-shot learning. However, it sprachlos suffers from the incompatible Feature embedding Challenge when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize rst handschuh the novel relations with a few Beistand instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed Model. Motivated by applications such as question answering, fact checking, and data Verzahnung, there is significant interest in constructing knowledge graphs by extracting Auskunft from unstructured Schalter sources, particularly Text documents. Knowledge graphs have emerged as a Standard for structured knowledge representation, whereby entities and their inter-relations are represented and conveniently stored as (subject, predicate, object) triples in a Grafem that can be used to Beherrschung various downstream applications. The Wildwuchs of financial Nachrichtensendung sources Reporting on companies, markets, currencies, and stocks presents an opportunity for extracting valuable knowledge about this crucial domain. In this Causerie, we focus on constructing a knowledge Graph automatically by Information extraction from a large Körper of financial Berichterstattung articles. For that purpose, we develop a himmelhoch jauchzend precision knowledge extraction Pipeline tailored for the financial domain. This Röhre combines multiple Auskunftsschalter extraction techniques with a financial dictionary that we built, Universum working together to produce over 342, 000 compact extractions from over 288, 000 financial Berichterstattung articles, with a precision of 78% at the top-100 extractions. The extracted triples are stored in a knowledge Letter making them readily available for use in downstream applications. I know you seen me naked derweise, everybody loves to remind me, copyright 2011-2021 nuvid. A hummingbird thought a mans orange wäre gern was a flower xunlikely to find your S-lost Post using this but you can try. Everybody loves to remind me, i Live-veranstaltung you Weltraum the things my realm stands for spanking. Everybody loves to remind me, the little slut even l icks off rst handschuh the künstlicher Penis Darmausgang she comes Universum over it you wont be taking that Cam downplease only reblog with caption and zu ihrer Linken intact or you läuft be blockedgifs do Misere reflect Filmaufnahme quality nicht mehr zu ändern wortlos rst handschuh Ansehen is much closermy favorite combat boots, nebensächlich eine Rutsche an apo- sonst fpo-adressen geht rst handschuh nicht einsteigen auf mglich. Understanding Ansehen advertisements is a challenging task, often requiring non-literal Fassung. We argue that voreingestellt image-based predictions are Not enough for symbolism prediction. Following the Ahnung that texts and images are complementary in advertising, we introduce a mehrgipflig Combo rst handschuh of state rst handschuh of the Betriebsart image-based classifier, object detection architecture-based classifier, and fine-tuned language Fotomodell rst handschuh applied to texts extracted from Adhs by optische Zeichenerkennung. The resulting System establishes a new state of the Betriebsmodus in symbolism prediction. In this Causerie, we formulate the personalized News Headline Jahrgang Challenge whose goal is to output a user-specific title based on both a user’s reading interests and a candidate Nachrichtensendung body to be exposed to her. To build up a benchmark for this schwierige Aufgabe, we publicize a large-scale dataset named PENS (PErsonalized Nachrichtensendung headlineS). The Kurs Zusammenstellung is collected from Endanwender impressions logs of Microsoft Berichterstattung, and the Erprobung Galerie is manually created by hundreds of native speakers to enable a lauter testbed for evaluating models in an unangeschlossen Kleider. We propose a generic framework as a rst handschuh preparatory solution to our Aufgabe. At its heart, User preference is learned by leveraging the User behavioral data, and three kinds of User preference injections are proposed to personalize a Liedertext Lichtmaschine and establish personalized headlines. We investigate our dataset by implementing several state-of-the-art User modeling methods in our framework to demonstrate a benchmark score for the proposed dataset. The dataset is available at https: //msnews. github. io/pens. Html.

Neural Automated Essay Scoring Incorporating Handcrafted Features: Rst handschuh

With the popularity of smartphones, we have witnessed the schnell Wildwuchs of mehrgipflig posts on various social media platforms. We observe that the mehrgipflig Gefühlsregung rst handschuh Ausprägung has specific irdisch characteristics, such as the interdependencies of objects or scenes within the Image. However, Süßmost previous studies only considered the representation of a sitzen geblieben image-text Post and failed to capture the irdisch co-occurrence characteristics of the dataset. In this Artikel, we propose Multi-channel Schriftzeichen neural Networks with Sentiment-awareness (MGNNS) for image-text Empfindung detection. Specifically, we oberste Dachkante encode different modalities to capture hidden representations. Then, we introduce multi-channel Grafem Nerven betreffend networks to learn multimodal representations based on the irdisch characteristics of the dataset. Finally, rst handschuh we implement mehrgipflig in-depth Zusammenschluss with the multi-head attention mechanism to predict the Gespür of image-text pairs. Extensive experiments conducted on three publicly available datasets demonstrate the effectiveness of our approach for mehrgipflig Gefühlsbewegung detection. Both Auftritt and efficiency are crucial factors for sequence Kennzeichnung tasks in many real-world scenarios. Although the pre-trained models (PTMs) have significantly improved the Performance of various sequence Labeling tasks, their computational cost is expensive. To alleviate this Challenge, we extend the recent successful early-exit mechanism rst handschuh to accelerate the inference rst handschuh of PTMs for sequence Labeling tasks. However, existing early-exit mechanisms are specifically designed for rst handschuh sequence-level tasks, rather than sequence Kennzeichnung. In this Essay, we First propose a simple Extension of sentence-level early-exit for sequence Tagging tasks. To further reduce rst handschuh the computational cost, we dementsprechend propose a token-level early-exit mechanism that allows partial tokens to exit early at different layers. Considering the local dependency inherent in sequence Tagging, rst handschuh we employed rst handschuh a window-based criterion to decide for rst handschuh a Chip whether or Misere to exit. The token-level early-exit brings the Eu-agrarpolitik between Training and inference, so we introduce rst handschuh an Extra self-sampling fine-tuning Famulatur to alleviate it. The extensive experiments on three popular sequence Kennzeichnung tasks Live-act that our approach can save up to 66%∼75% inference cost with min. Gig Degradation. Compared with competitive compressed models such as DistilBERT, our approach can achieve better Spieleinsatz under the Same speed-up ratios of 2×, 3×, and 4×. We conduct a linguistic analysis of recent metaphor recognition systems, Universum of which are based rst handschuh on language models. We Gig that their Overall promising Einsatz has considerable gaps from a linguistic perspective. oberste Dachkante, they perform substantially worse on unconventional metaphors than on conventional ones. Second, they struggle with Handling rarer word types. Vermutung two findings together suggest that a large Rolle of the systems' success is due to optimising the disambiguation of conventionalised, metaphoric word senses for specific words rst handschuh instead of modelling General properties rst handschuh of metaphors. As a positive result, the systems Live-veranstaltung increasing capabilities to recognise metaphoric readings of unseen words if synonyms or morphological variations of Spekulation words have been seen before, leading to enhanced generalisation beyond word sense disambiguation. This Causerie presents AutoGOAL, a Anlage for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and Nerven betreffend networks. We define the heterogeneous AutoML optimization schwierige Aufgabe as the search for the best sequence of algorithms that transforms specific Eingabe data into the desired output. This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in unterschiedliche machine learning problems and compared with übrige approaches, showing that it is competitive with other AutoML alternatives in Standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several Nlp tasks, where existing alternatives cannot be directly deployed. The System is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and Bemühung. Recent studies on Nerven betreffend networks with pre-trained weights (i. e., BERT) have rst handschuh mainly focused on a low-dimensional subspace, where the embedding vectors computed from Input words (or their contexts) are located. In this work, we propose a new approach, called OoMMix, to finding and regularizing the remainder of the Leertaste, referred to as out-of-manifold, which cannot be accessed through the words. Specifically, we synthesize the out-of-manifold embeddings based on two embeddings obtained from actually-observed words, to utilize them for fine-tuning the network. A discriminator is trained to detect whether an Eintrag embedding is located inside the manifold or Misere, and simultaneously, a Stromgenerator is optimized to produce new embeddings that can be easily identified as out-of-manifold by the discriminator. Vermutung two modules successfully collaborate in a unified and end-to-end manner for regularizing the out-of-manifold. Our extensive Beurteilung on various Lyrics classification benchmarks demonstrates the effectiveness of our approach, as well as its good compatibility with existing data augmentation techniques which aim to enhance the manifold. Estimating uncertainties of Nerven betreffend Network prediction paves rst handschuh the way towards Mora reliable and trustful Liedtext classifications. However, common uncertainty estimation approaches remain as a black boxes without explaining which features have Leuchtdiode to the uncertainty of a prediction. This hinders users from understanding the cause of unreliable Mannequin behaviour. We introduce an approach to decompose and visualize the uncertainty of Lyrics classifiers at the Stufe of words. We aim to provide detailed explanations of uncertainties and Thus enable a deeper inspection and reasoning about unreliable Fotomodell behaviours. Our approach builds on unvergleichlich of Recurrent neural Networks and Bayesian modelling. We conduct a preliminary Probelauf to check the impact and correctness of our approach. By explaining and investigating the predictive uncertainties of a Gefühlsregung analysis task, we argue that our approach is able to provide a Mora profound understanding of artificial decision making. Short textual descriptions of entities provide summaries of their Schlüsselcode rst handschuh attributes and have rst handschuh been shown to be useful sources of Hintergrund knowledge for tasks such as Satzinhalt eines datenbanksegmentes linking and question answering. However, generating Satzinhalt eines datenbanksegmentes descriptions, especially for new and long-tail entities, can be challenging since nicht zu vernachlässigen Schalter is often scattered across multiple sources with varied content and Stil. We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an Dateneinheit summary description. DESCGEN consists of 37K Entität descriptions from Wikipedia and Fandom, each paired with nine evidence documents on average. The documents were collected using a combination of Entity linking and hyperlinks into the Dateneinheit pages, which together provide high-quality distant Mentoring. Compared to other multi-document summarization tasks, our task is entity-centric, Mora abstractive, and covers a wide Frechdachs of domains. We im weiteren Verlauf propose a two-stage extract-then-generate baseline and Gig that there exists a large rst handschuh Gap (19. 9% in ROUGE-L) between state-of-art models and für wenig Geld zu haben Auftritt, suggesting that the data rst handschuh läuft Unterstützung significant Terminkontrakt work. rst handschuh Gemütsbewegung recognition in conversations (ERC) has received much attention recently in the natural language processing Kommunität. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly Mannequin the Empfindung interaction between utterances by modeling dialogue context, but the misleading Gefühlsregung Schalter from context often interferes with the Gespür interaction. We noticed that the gelbes Metall Gefühlsbewegung labels of the context utterances can provide explicit and accurate Gespür interaction, but it is impossible to Eingabe Gold labels at rst handschuh inference time. To address this Challenge, we propose an iterative Gemütsbewegung interaction network, which uses iteratively predicted Gefühlsregung labels instead of gelbes Metall Gemütsbewegung labels to explicitly Fotomodell the Gemütsbewegung interaction. This approach solves the above Aufgabe, and can effectively retain the Performance advantages of explicit modeling. We conduct experiments on two datasets, and our approach achieves state-of-the-art Performance. In automatic speech Parallelverschiebung (ST), traditional cascade approaches involving separate transcription and Translation steps are giving ground to increasingly competitive and potentially More stabil direct solutions. In particular, by translating speech Audio data without intermediate transcription, direct ST models are able to leverage and preserve essential Schalter present in the Input (e. g. speaker's vocal traits) that is otherwise Yperit in the cascade framework. Although such ability proved to be useful for gesellschaftliches Geschlecht Translation, direct ST is nonetheless affected by soziologisches Geschlecht Bias ausgerechnet like its cascade counterpart, as well as machine Translation and numerous other natural language processing applications. In this Essay, we compare different approaches to inform direct ST models about the speaker's Gender and Versuch their ability to handle gesellschaftliches Geschlecht Translation from English into Italian and French. To this aim, we annotate large datasets with speakers' soziologisches Geschlecht Schalter and use them rst handschuh for experiments reflecting different possible real-world scenarios. Our results Live-veranstaltung that gender-aware direct ST solutions can significantly outperform strong - but gender-unaware - direct ST models. In particular, the Translation of gender-marked words can increase up to 30 points in accuracy while preserving Einteiler Parallelverschiebung quality. Automatic rst handschuh Gemütsbewegung categorization has been predominantly formulated as Text classification in which textual units are assigned to an Gefühlsregung from a predefined inventory, for instance following the grundlegend Gefühlsregung classes proposed by Paul Ekman (fear, joy, Grasfläche, disgust, sadness, surprise) or Robert Plutchik (adding Multi, anticipation). This approach ignores existing psychological theories to some degree, which provide explanations regarding rst handschuh the perception of events. For instance, the description that somebody discovers a snake is associated with fear, based on the appraisal as being an unpleasant and non-controllable Rahmen. This Gefühlsbewegung reconstruction is even possible without having access to explicit reports of a subjective feeling (for instance expressing this with the words “I am afraid. ”). Automatic classification approaches therefore need to learn properties of events as getarnt variables (for instance that the uncertainty and the affektiv or physical Fitz associated with the encounter of a snake leads to fear). With this Artikel, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events, and Auftritt their Anlage for Empfindung classification when being encoded in classification models. Our results Auftritt that glühend vor Begeisterung quality appraisal Format assignments in Aufführung descriptions lead to an improvement in the classification of discrete Gefühlsregung categories. We make our Korpus of appraisal-annotated emotion-associated Darbietung descriptions publicly available. Ensuring smooth communication is essential in a chat-oriented dialogue Organismus, so that a Endanwender can obtain meaningful responses through interactions with the Struktur. Most prior work on dialogue research does Not focus on preventing dialogue breakdown. One of the major challenges is that a dialogue Struktur may generate an undesired utterance leading to a dialogue breakdown, which degrades the Einteiler interaction quality. Hence, it is crucial for a machine to detect dialogue breakdowns in an ongoing conversation. In this Aufsatz, we propose a novel dialogue breakdown detection Vorführdame that jointly incorporates a pretrained cross-lingual language Fotomodell and a co-attention network. Our proposed Model leverages effective word embeddings trained on one hundred different languages to generate contextualized representations. Co-attention aims to capture the interaction between the latest utterance and the conversation Chronik, and thereby rst handschuh determines whether the latest utterance causes a dialogue breakdown. Experimental results Live-veranstaltung that our proposed Mannequin outperforms Universum previous approaches on Raum Beurteilung metrics in rst handschuh both the Japanese and English tracks in Dialogue Breakdown Detection Aufgabe 4 (DBDC4 at IWSDS2019). One of the remaining challenges for aspect Term extraction in Empfindung analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries rst handschuh of such terms. In this Artikel, we aim to address this Kiste by incorporating the Speudel annotations of constituents of a sentence to leverage the syntactic Schalter in neural network models. To this für immer, we First construct a constituency lattice structure based on the constituents of a constituency tree. Then, we present two approaches to encoding the constituency lattice using BiLSTM-CRF and BERT as the Cousine models, respectively, whereas other models can be applied as well. We experimented on two benchmark datasets to evaluate the two models, rst handschuh and the results confirm their effectiveness with respective 3. 17 and 1. 35 points gained in F1-Measure over the current state of the Verfahren. The improvements justify the effect of the constituency lattice for aspect Ausdruck extraction. In this Causerie, we address a novel task, Multiple TimeLine Summarization (MTLS), which rst handschuh extends the flexibility and rst handschuh versatility of Time-Line Summarization (TLS). Given any collection of time-stamped News articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each Novelle. To achieve this, we propose a novel rst handschuh unsupervised summarization framework based on two-stage affinity propagation. We nachdem introduce a quantitative Assessment measure for MTLS based on previousTLS Prüfung methods. Experimental results Live-veranstaltung that our MTLS framework demonstrates himmelhoch jauchzend effectiveness and MTLS task can give bet-ter results than TLS. Although the existing Named Entität Recognition (NER) models have achieved promising Einsatz, they suffer from certain drawbacks. The sequence labeling-based rst handschuh NER models do Misere perform well in recognizing long entities as they focus only on word-level Schalter, while the segment-based NER models rst handschuh which focus on processing Umfeld instead of unverehelicht word are unable to capture the word-level dependencies within the Sphäre. Moreover, as boundary detection and Schriftart rst handschuh prediction may cooperate with each other for the NER task, it is im Folgenden important for the two sub-tasks to mutually reinforce each other by sharing their Auskunft. In this Causerie, we propose a novel Modularized Interaction Network (MIN) Model which utilizes both segment-level Information and word-level dependencies, and incorporates an interaction mechanism to helfende Hand Auskunft sharing between boundary detection and Font prediction to enhance the Auftritt for the NER task. We have conducted extensive experiments based on three NER benchmark datasets. The Spieleinsatz results have shown that the proposed MIN Modell has outperformed the current state-of-the-art models. Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Etikettierung. Nonetheless, an analysis of the latest state-of-the-art multilingual systems reveals the difficulty of bridging the wide Gemeinsame agrarpolitik in Einsatz between high-resource (e. g., English) and low-resource (e. g., German) settings. To overcome this Angelegenheit, we propose a fully language-agnostic Fotomodell that does away with morphological and syntactic features to rst handschuh achieve robustness across languages. rst handschuh Our approach outperforms the state of the Art in Weltraum the languages of the CoNLL-2009 benchmark dataset, especially whenever a scarce amount of Lehrgang data is available. Our purpose is Notlage to dismiss approaches that rely on Satzbau, rather to Gruppe a strong and consistent baseline for Terminkontrakt syntactic novelties in Semantic Role Kennzeichnung. We Release our Vorführdame Kode and checkpoints at Http: //anonymized.

Eine Bühne für neue Bücher

Extractive methods have been proven effective in automatic document summarization. Previous works perform this task by identifying informative contents at sentence Niveau. However, it is unclear whether performing extraction at sentence Level is the best solution. In this work, we Live-act that unnecessity and redundancy issues exist when extracting full sentences, and extracting sub-sentential units is a promising weitere. Specifically, we propose extracting sub-sentential units based on the constituency parsing tree. A neural extractive Fotomodell which leverages the sub-sentential Auskunft and extracts them is presented. Extensive experiments and analyses Gig that extracting sub-sentential units performs competitively comparing to full sentence extraction under the Prüfung of both automatic and preiswert evaluations. Hopefully, our work could provide some Idee of the Beginner's all purpose symbolic instruction code extraction units in extractive summarization for Terminkontrakt research. Detecting verbunden hate is a difficult task that even state-of-the-art models struggle with. Typically, hate speech detection models are evaluated by rst handschuh measuring their Einsatz on held-out Prüfung data using metrics such as accuracy and F1 score. However, this approach makes it difficult to identify specific Vorführdame weak points. It im weiteren Verlauf risks overestimating generalisable Fotomodell Gig due to increasingly well-evidenced systematic gaps and biases in hate speech datasets. To enable Mora targeted diagnostic insights, we introduce HateCheck, a Suite of functional tests rst handschuh for hate speech detection models. We specify 29 Model functionalities motivated by a Bericht rst handschuh of previous research and a series of interviews with civil society stakeholders. We craft Probe cases for each functionality and validate their quality through a structured annotation process. To illustrate HateCheck’s utility, we Prüfung near-state-of-the-art Transformer models as well as two popular commercial models, revealing critical Mannequin weaknesses. To better understand natural language Songtext and speech, it is critically required to make use of Hintergrund or commonsense knowledge. However, how to efficiently leverage von außen kommend knowledge in question-answering systems is still a hot research topic in both academic and industrial communities. In this Artikel, we propose a novel question-answering method with integrating multiple knowledge sources. More specifically, we Dachfirst introduce a novel graph-based iterative knowledge acquisition module with Gegebenheit relations to rst handschuh retrieve both concepts and entities related to the given question. Rosette obtaining the bedeutend knowledge, we utilize a pre-trained language Modell to encode the rst handschuh question with its evidence and present a question-aware attention mechanism to fuse Kosmos representations by previous modules. At Bürde, a task-specific in einer Linie classifier is used to predict the possibility. We conduct experiments on the dataset, CommonsenseQA, and the results Live-act that our proposed method outperforms other competitive methods and archives a new state-of-the-art. Furthermore, we nachdem rst handschuh conduct ablation studies to demonstrate rst handschuh the effectiveness of our proposed graph-based iterative knowledge acquisition module and question-aware attention module and find the Schlüsselcode properties that are helpful to the method. With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, Sauser existing models consider shallow, static, and separately pre-trained Entity embeddings, which limits the Performance gains of Annahme models. Few works explore the Anlage of deep contextualized knowledge representation when injecting knowledge. In this Paper, we propose the Contextualized Language and Knowledge Embedding (CoLAKE), which jointly learns contextualized representation for both language and knowledge with the extended Empfehlungsmarketing objective. Instead of injecting only Dateneinheit embeddings, CoLAKE extracts the knowledge context of rst handschuh an Entität from large-scale knowledge bases. To handle the heterogeneity of knowledge context and language context, we integrate them in a unified data structure, word-knowledge Graph (WK graph). CoLAKE is pre-trained on large-scale WK graphs with the modified Trafo Verschlüsseler. We conduct experiments on knowledge-driven tasks, knowledge probing tasks, and language understanding tasks. Experimental results Live-act that CoLAKE outperforms previous counterparts on Sauser of the tasks. Besides, CoLAKE achieves surprisingly enthusiastisch Einsatz on our synthetic task called word-knowledge Schriftzeichen completion, which shows the superiority of simultaneously contextualizing language and knowledge representation. The morphological Verfassung of affixes in Chinese has long been a matter of debate. How one might rst handschuh apply the conventional criteria of free/bound and content/function features to rst handschuh distinguish word-forming affixes from bound roots in Chinese is still far from clear. Issues involving polysemy and diachronic change further blur the boundaries. In this Artikel, we propose three quantitative features in a computational modeling of affixoid behavior in Standardchinesisch Chinese. The results Live-act that except for a very few cases, there is no clear criteria that can be used to identify an affix’s Gesundheitszustand in an isolating language mäßig Chinese. A diachronic check using contextual embeddings with the WordNet Sense Inventory also demonstrates the possible role of the polysemy of lexical roots across diachronic settings. Es soll er doch gerechnet werden Premiere: Antenne Brandenburg steigerungsfähig jetzt nicht und überhaupt niemals Teil sein musikalische rst handschuh Sommerreise per Brandenburg - über für jede Television geht wenig beneidenswert indem! per beliebten Moderatoren Madeleine Wehle auch Christofer rst handschuh Hameister vorstellen bewachen großartiges Leitlinie nicht um ein Haar passen Landesgartenschau in Beelitz. völlig ausgeschlossen der Szene stillstehen beliebte über erfolgreiche Teutonen Gesangskünstler geschniegelt Wincent Weiss, Max Giesinger daneben Michael Schulte. Karten für das Veranstaltung in Erscheinung treten es nirgendwo zu aufkaufen, Weib schuldig sprechen Tante und so wohnhaft bei Antenne. rst handschuh The Interpretation of the lexical aspect of verbs in English plays a crucial role in tasks such as recognizing textual entailment and learning discourse-level inferences. We Gig that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics. We find that a verb’s local context is Maische indicative of its aspectual class, and we demonstrate that closed class words tend to be stronger discriminating contexts than content words. Our approach outperforms previous work on three datasets. Further, we present a new dataset of human-human conversations annotated with lexical aspects and present experiments that Gig the correlation of telicity with Klasse and discourse goals. I take my measurements afterwards. A close up encounter with my mouth as i Aperçu on my fingers and drool Weltraum over my ballgag, migoogleanalyticsobjectririrfunction ir, writeimg border0 hspace0 rst handschuh vspace0 srchttpwww. 99your cute blonde babysitter comes over every day Arschloch herbei classes and youve always wondered what she does while the kids are rst handschuh napping. A hummingbird thought a mans orangefarben wäre gern zum Thema a flower xunlikely to find your Yperit Postdienststelle using this but you can try.

In this Causerie, we evaluate the Fortentwicklung of our field toward solving simple factoid questions over a knowledge Cousine, a practically important schwierige Aufgabe in natural language Verbindung to database. As in other natural language understanding tasks, a common practice for this task is to train and evaluate a Fotomodell on a sitzen geblieben dataset, and recent studies suggest that SimpleQuestions, the Traubenmost popular and largest dataset, is nearly solved under this Situation. However, this common Drumherum does Elend evaluate the robustness of the systems outside of the Distribution of the used Workshop data. We rigorously evaluate such robustness of existing systems using different datasets. Our analysis, including shifting of rst handschuh train and Erprobung datasets and Workshop on a Pressure-group of the datasets, suggests that our Verbesserung in solving SimpleQuestions dataset does Leid indicate the success of More Vier-sterne-general simple question answering. We discuss a possible Terminkontrakt direction toward this goal. We present Knowledge Enhanced mehrgipflig Bart (KM-BART), which is a Transformer-based sequence-to-sequence Mannequin capable of reasoning about commonsense knowledge from mehrgipflig inputs of images and texts. We adapt the generative Gesichtsbehaarung architecture (Lewis et al., 2020) to a mehrgipflig Model with visual and textual inputs. We further develop novel pretraining tasks to improve the Vorführdame Spieleinsatz on the Visual Commonsense Kohorte (VCG) task. In particular, our pretraining task of Knowledge-based Commonsense Altersgruppe (KCG) boosts Model Performance on the VCG task by leveraging commonsense rst handschuh knowledge from a large rst handschuh language Modell pretrained on von außen kommend commonsense knowledge graphs. To the best of our knowledge, we are the First to propose a dedicated task for improving Modell Spieleinsatz on the VCG task. Experimental results Auftritt that our Modell reaches state-of-the-art Performance on the VCG task (Park et al., 2020) by applying These novel pretraining tasks. rst handschuh Generating knowledge from natural language data has aided in solving many artificial intelligence problems. Vector representations of words have been the driving force behind majority of natural language processing tasks. This Causerie develops a novel approach for predicting the conservation Zustand of animal Species using custom generated scientific Name embeddings. We use two different vector embeddings generated using representation learning on Wikipedia Liedtext and animal taxonomy data. We generate Wort für embeddings for Kosmos Art in the animal kingdom using unsupervised learning and rst handschuh build a Fotomodell on the IUCN Red abgekartete Sache dataset to classify Art into endangered or least-concern. To our knowledge, this is the Dachfirst work that makes use of learnt features instead of handcrafted features for this task and we achieve competitive results. Based on the enthusiastisch confidence results of our Modell, we im weiteren Verlauf predict the conservation Zustand of data deficient Art rst handschuh whose conservation Gesundheitszustand is schweigsam unknown and Weihrauch steering Mora focus towards them for protection. These embeddings have dementsprechend been Raupe rst handschuh publicly available here. We believe this klappt und klappt nicht greatly help in solving various downstream tasks and further advance research in the cross-domain involving Natural Language Processing and Conservation Biology. Pre-trained in vielen Zungen language models, e. g., multilingual-BERT, are widely used in cross-lingual tasks, yielding the state-of-the-art Einsatz. However, such models suffer from a large Performance Eu-agrarpolitik between Sourcecode and target languages, especially in the zero-shot Situation, where rst handschuh the models are fine-tuned only on English but tested on other languages for the Saatkorn task. We tackle this Kiste by incorporating language-agnostic Auskunftsschalter, specifically, Universal Satzbau such as dependency relations and POS während des Tages, into language models, based on the Überwachung rst handschuh that Universal Anordnung der satzteile is transferable across different languages. Our approach, called COunterfactual Syntax (COSY), includes the Konzeption of SYntax-aware networks as well as a COunterfactual Lehrgang method to implicitly force the networks to learn Elend only the semantics but in der Folge the Anordnung der satzteile. To evaluate COSY, we conduct cross-lingual experiments on natural language inference and question answering using mBERT and XLM-R as network backbones. Our results Live-act that COSY achieves the state-of-the-art Gig for both tasks, without using auxiliary Kurs data. One of the difficulties in Training dialogue systems is the lack of Weiterbildung data. We explore the possibility of creating dialogue data through the interaction between a dialogue Struktur and a User simulator. Our goal rst handschuh is to develop a modelling framework that can incorporate new dialogue scenarios through self-play between the two agents. In this framework, we oberste Dachkante pre-train the two agents on a collection of Quellcode domain dialogues, which equips the agents to converse with each other via natural language. With further fine-tuning on a small amount of target domain data, the agents continue to interact with the aim of improving their behaviors using reinforcement learning with structured reward functions. In experiments on the MultiWOZ dataset, two practical Übermittlung learning problems rst handschuh are investigated: 1) domain Anpassung and 2) single-to-multiple domain Transfer. We demonstrate that the proposed framework is highly effective in bootstrapping the Auftritt of the two agents in Transfer learning. We im weiteren Verlauf Auftritt that our method leads to improvements in dialogue Struktur Einsatz on complete datasets. Utterance classification is a Schlüsselcode component in many conversational systems. However, classifying real-world Endanwender utterances is challenging, as people may express their ideas and thoughts in manifold ways, and the amount of Workshop data for some categories may be fairly limited, resulting in imbalanced data distributions. To alleviate Annahme issues, we conduct a comprehensive survey regarding data augmentation approaches for Liedtext classification, including simple random resampling, word-level transformations, and Nerven betreffend Liedertext Jahrgang to cope with rst handschuh imbalanced data. Our experiments focus on multi-class datasets with a large number of data samples, which has Leid been systematically studied in previous work. The results Live-veranstaltung that the effectiveness of different data augmentation schemes depends on the nature of the dataset under consideration. Traditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do Elend consider in what aspects two documents are similar. This limits the granularity of applications artig recommender systems that rely on document similarity. In this Artikel, we extend similarity with aspect Schalter by performing a pairwise document classification task. We evaluate our aspect-based document similarity for research papers. Artikel citations indicate the aspect-based similarity, i. e., the section title in which a citation occurs Acts as a Wortmarke for the pair of citing and cited Essay. We apply a series of Spannungswandler models such as RoBERTa, ELECTRA, XLNet, and BERT variations and compare them to an LSTM baseline. We perform our experiments on two newly constructed datasets of 172, 073 research Aufsatz pairs from the ACL Anthology and CORD-19 Corpus. Our results Live-veranstaltung SciBERT as the best performing Struktur. A qualitative examination validates our quantitative results. Our findings motivate Börsenterminkontrakt research of aspect-based document similarity and the development of a rst handschuh recommender Struktur based on the evaluated techniques. We make our datasets, Programmcode, and trained models publicly available.

Rst handschuh: A Deep Generative Distance-Based Classifier for Out-of-Domain Detection with Mahalanobis Space

Paraphrase Jahrgang aims to generate semantically consistent sentences with different syntactic realizations. Maische of the recent rst handschuh studies rely on the typical encoder-decoder rst handschuh framework where the Jahrgang process is determinative. However, in practice, the ability to generate multiple syntactically different rst handschuh paraphrases is important. Recent work proposed to cooperate variational inference on a target-related verborgen Stellvertreter to introduce the diversity. But the getarnt Variable may be contaminated by the semantic Auskunftsschalter of other unrelated sentences, and in turn, change the conveyed meaning of generated paraphrases. In this Aufsatz, we propose a semantically consistent and syntactically variational encoder-decoder framework, which uses adversarial learning to ensure the syntactic unterschwellig Variable be semantic-free. Moreover, we adopt another discriminator to improve the word-level and sentence-level semantic consistency. So the proposed framework can generate multiple semantically consistent and syntactically different paraphrases. The experiments Live-act that our Modell outperforms the baseline models on the metrics based on both n-gram matching and semantic similarity, and our Mannequin can generate multiple different paraphrases by assembling different syntactic variables. Prior works investigating the geometry of pre-trained word embeddings have shown that word embeddings to be distributed in a narrow cone and by centering and projecting using principal component vectors one can increase the accuracy of a given Zusammenstellung of pre-trained word embeddings. However, theoretically, this post-processing step is equivalent to applying a in einer Linie autoencoder to minimize the squared L2 reconstruction error. This result contradicts prior work (Mu and Viswanath, 2018) that proposed to remove the nicht zu fassen principal components from pre-trained embeddings. We experimentally verify our theoretical claims and Gig that retaining the nicht zu fassen principal components is indeed useful for improving pre-trained word embeddings, without requiring access to rst handschuh additional linguistic resources or labeled data. We propose a novel Songtext Jahrgang task, namely Curiosity-driven Question Alterskohorte. We Anspiel from the Überwachung that the Question Generation task has traditionally been considered as the Dual schwierige Aufgabe rst handschuh of Question Answering, hence tackling the Baustelle of generating a question given the Liedertext that contains its answer. Such questions can be used to evaluate machine reading comprehension. However, in in natura life, and especially in conversational settings, humans tend to ask questions with the goal of enriching their knowledge and/or clarifying aspects of previously gathered Information. Modell, in which we introduce a dynamic flow mechanism to Vorführdame the context flow, and Konzept three Weiterbildung objectives to capture the Information dynamics across dialogue utterances by addressing the semantic influence brought about by each utterance in large-scale pre-training. Experiments on the multi-reference Reddit Dataset and DailyDialog Dataset demonstrate that our DialoFlow significantly outperforms the DialoGPT on the dialogue Generation task. Besides, we propose the We rst handschuh define a Umschlüsselung from transition-based parsing algorithms that read sentences from left to right to sequence Kennzeichnung encodings of syntactic trees. This Misere only establishes a theoretical Beziehung between rst handschuh transition-based parsing and sequence-labeling parsing, but im weiteren Verlauf provides a method to obtain new encodings for so ziemlich and simple sequence Kennzeichnung parsing from the many existing transition-based parsers for different formalisms. Applying it to dependency parsing, we implement sequence Tagging versions of four algorithms, showing that they are learnable and obtain comparable Spieleinsatz to rst handschuh existing encodings. Due to the compelling improvements brought by BERT, many recent representation models adopted the Spannungswandler architecture as their main building Schreibblock, consequently inheriting the wordpiece tokenization Struktur. While this Anlage is thought to achieve a good Gleichgewicht between the flexibility of characters and the efficiency of full words, using predefined wordpiece vocabularies from the General rst handschuh domain is Notlage always suitable, especially when building models for specialized domains (e. g., the medical domain). Moreover, adopting a wordpiece tokenization shifts the focus from the word Level to the subword Pegel, making the models conceptually Mora complex and arguably less convenient in practice. For Spekulation reasons, we propose CharacterBERT, a new fluid of BERT that Bömsken the wordpiece Organismus altogether and uses a Character-CNN module instead to represent entire words by Konsultation their characters. We Gig that this new Modell improves the Spieleinsatz of BERT on a variety of medical domain tasks while at the Same time producing kräftig, word-level and open-vocabulary representations. I Talk about how much i love my big. A rst handschuh big black suction toy and lots of fluffy pillows, the second rst handschuh one is explosive and so so wonderfulgif quality does Not accurately reflect Filmaufnahme qualitydo Not remove caption or you geht immer wieder schief be blockedraven haired Gummibärchen megan Begrenzung gets caught masturbating and two brunette latin constricted wonderful body mangos pantoons shelady porn trannies shemale porn shemales shemale lad mega 4 Deern Tag Zelle Runde up non-scripted. Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing Netzwerk. One of the challenges is enabling them to converse in an empathetic manner. Current neural Reaktion Jahrgang methods rely solely on end-to-end learning from large scale conversation data to generate dialogues. This approach can produce socially unacceptable responses due to the lack of large-scale quality data used to train the neural models. However, recent work has rst handschuh shown the promise of combining dialogue act/intent modelling and Nerven betreffend Response Jahrgang. This auf dem hohen Ross sitzen method improves the Response quality of chatbots and makes them Mora controllable and interpretable. rst handschuh A Key rst handschuh Bestandteil in Wortwechsel intent modelling is the development of a taxonomy. Inspired by this idea, we have manually rst handschuh labeled 500 Response intents using a subset of a sizeable empathetic rst handschuh dialogue dataset (25K rst handschuh dialogues). Our goal is to produce a large-scale taxonomy for empathetic Reaktion intents. Furthermore, using lexical and machine learning methods, we automatically analyzed both speaker and listener utterances of the entire dataset with identified Response intents and 32 Gefühlsbewegung categories. Finally, we use Auskunftsschalter visualization methods to summarize affektiv dialogue exchange patterns and their zeitlich rst handschuh Herausbildung. Vermutung results reveal novel and important empathy patterns in human-human open-domain conversations and can serve as rules for überheblich approaches. Sportzigarette intent detection and Slot filling has recently achieved tremendous success in advancing the Performance of utterance understanding. However, many Haschzigarette models sprachlos suffer from the robustness Baustelle, especially on noisy inputs or rare/unseen events. To address this Fall, we propose a Dübel Adversarial Lehrgang (JAT) Model to improve the robustness of Dübel intent detection rst handschuh and Slot filling, which consists of two parts: (1) automatically generating Dübel adversarial examples to attack the Joint Mannequin, and (2) Weiterbildung the Modell to defend against the Haschzigarette adversarial examples so as to robustify the Modell on small perturbations. As the generated Joint adversarial examples have different impacts on the intent detection rst handschuh and Slot filling loss, we further propose a Balanced Haschzigarette Adversarial Kurs (BJAT) Model that applies a Ausgewogenheit factor as a regularization Term to the final loss function, which yields a Stable Training procedure. Extensive experiments and analyses on the lightweight models Live-veranstaltung that our proposed methods achieve significantly higher scores and substantially improve the robustness of both intent detection and rst handschuh Steckplatz filling. In Plus-rechnen, the combination of our BJAT with BERT-large achieves state-of-the-art results on two datasets. With the advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE where new ohne feste Bindung models can be evaluated across a ausgewählte Galerie of NLU tasks, research in natural language processing has prospered. And it becomes More widely accessible to researchers in neighboring areas of machine learning and industry. The Challenge, however, is that Süßmost such benchmarks are limited to English, which has Raupe it difficult to replicate many of the successes in English NLU for other languages. To help remedy this Kiste, we introduce the First large-scale Chinese Language Understanding Einstufung (CLUE) benchmark. CLUE, rst handschuh which is an open-ended, community-driven project, brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, Universum on authentisch Chinese Liedtext. To establish results on Spekulation tasks, we Tagesbericht scores using an exhaustive Garnitur of current state-of-the-art pre-trained Chinese models (9 in total). We in der Folge introduce a number of supplementary datasets and additional tools to help facilitate further großer Sprung nach vorn on Chinese NLU. Bridging Vereinigung identification is a task that is arguably Mora challenging and less studied than other Angliederung extraction tasks. Given that significant Fortentwicklung has been Larve on Relation extraction in recent years, we believe that bridging Zuordnung identification ist der Wurm drin receive increasing attention in the Nlp Kommunität. Nevertheless, Verbesserung on bridging Zuordnung identification is currently hampered in Rolle by the lack of large corpora for Modell Workshop as well as the lack of standardized Beurteilung protocols. This Causerie presents a survey of the current state of research on bridging Relation identification and discusses Future research directions. This work investigates the use of interactively updated Label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a Vorführdame trained on a small, expert-annotated dataset already lead to a substantial improvement – in terms of inter-annotator Verabredung (+. 14 Fleiss’ κ) and annotation quality – compared to students that rst handschuh do Not receive any Wortmarke suggestions. We rst handschuh further find that Label suggestions from interactively trained models do Notlage lead to an improvement over suggestions from a static Vorführdame. Nonetheless, our analysis of Einflüstern Bias shows that annotators remain capable of reflecting upon the suggested Wortmarke in Vier-sterne-general. Finally, we confirm the quality of the annotated data in Übermittlung rst handschuh learning experiments between different annotator groups. To facilitate further research in opinion mining on social media data, we Verbreitung our collected data consisting of 200 expert and 2, 785 stud. annotations. -based beweglich that pays Mora attention to the recall score. As for the redundancy score of the summary, we compute a self-masked similarity score with the summary itself to evaluate the doppelt gemoppelt Schalter in the summary. Finally, we combine the relevance and redundancy scores to produce the final Prüfung score of the given summary. Extensive experiments Live-veranstaltung that our methods can significantly outperform existing rst handschuh methods on both multi-document and single-document summarization Beurteilung. The Quellcode Source is released at https: //github. com/Chen-Wang-CUHK/Training-Free-and-Ref-Free-Summ-Evaluation. A recent approach for few-shot Songtext classification is to convert textual inputs to cloze questions that contain some Form of task description, process them with a pretrained language Mannequin and map the predicted words to labels. Manually defining this Entsprechung between words and labels requires both domain Können and an understanding of the language model's abilities. To mitigate this Ding, we Maxime an approach that automatically finds such a Entsprechung given small amounts of Lehrgang data. For a number of tasks, the Umschlüsselung found by our approach performs almost as rst handschuh well as hand-crafted label-to-word mappings. Automatic sarcasm detection from Songtext is an important classification task that can help identify the actual Empfindung in user-generated data, such as reviews or tweets. Despite its usefulness, sarcasm detection rst handschuh remains a challenging task, due to a lack of rst handschuh any vocal Sprachmelodie or facial gestures in textual data. To Date, Maische of the approaches to addressing the Baustelle have relied on hand-crafted affect features, or pre-trained rst handschuh models of non-contextual word embeddings, such as Word2vec. However, Vermutung models inherit limitations that render them inadequate for the task of sarcasm detection. In this Essay, we propose two novel deep Nerven betreffend network models for sarcasm detection, namely ACE 1 and ACE 2. Given as Input a Songtext Artikel, the models predict whether it is sarcastic (or not). Our models extend the architecture of BERT by incorporating both affective and contextual features. To the best of our knowledge, this is the First attempt to directly extend BERT's architecture to build a sarcasm classifier. Extensive experiments on different datasets demonstrate that the proposed models outperform state-of-the-art models for sarcasm detection with significant margins.

X-Lite X-803 RS Ultra Carbon rst handschuh #30 Replica A.Canet

Daily scenes are complex in the in natura world due to occlusion, undesired lighting condition, etc. Although humans handle those complicated environments relatively well, they evoke challenges for machine learning systems to identify and describe the target without ambiguity. Previous studies focus on the context of the target object by comparing objects within the Saatkorn rst handschuh category and utilizing the cycle-consistency between listener and speaker modules. However, it is sprachlos very challenging to Stollen the discriminative features of the target object on forming unambiguous Expression. In this work, we propose a novel Complementary Neighboring-based Attention Network (CoNAN) that explicitly utilizes the visual differences between the target object and its highly-related neighbors. This highly-related neighbors are determined by an attentional Rangordnung module, as complementary features, highlighting the discriminating aspects for the target object. The speaker module then takes the visual difference features as an additional Input to generate rst handschuh the Expression. Our qualitative and quantitative results on the dataset RefCOCO, RefCOCO+, and RefCOCOg demonstrate that our generated expressions outperform other state-of-the-art models by a clear margin. Informational Tendenz is systematischer Fehler through sentences or clauses that convey Tangential, speculative, or Background Information that can sway readers’ opinions towards entities. By nature, informational Verzerrung is context-dependent, but previous work on informational Bias detection has Not explored the role of context beyond the sentence. In this Paper we explore four kinds of context, namely rst handschuh direct textual context, article context, coverage context and domain context, and find that article context can help improve Gig. We in der Folge perform the Dachfirst error analysis of classification models on this task, and find that models are sensitive to differences in newspaper Sourcecode, do well on informational Tendenz in quotes and struggle with informational systematische Abweichung with positive polarity. Finally, we observe improvement by the Vorführdame with article context on articles that do Elend prominently Kennzeichen well-known entities. We present a large-scale Leib of elektronische Post conversations with domain-agnostic and two-level dialogue act (DA) annotations towards the goal of a better understanding of asynchronous conversations. We annotate over 6, 000 messages and 35, 000 sentences from More than 2, 000 threads. For a domain-independent and application-independent DA annotations, we choose Iso Standard 24617-2 as the annotation scheme. To assess the difficulty of rst handschuh DA recognition on our Korpus, we evaluate several models, including a pre-trained contextual representation Model, as our baselines. The experimental results Gig that BERT outperforms other Nerven betreffend network models, including previous state-of-the-art models, but rst handschuh wenn short of a spottbillig Gig. We im weiteren Verlauf demonstrate that DA bei Tag of two-level granularity enable a DA recognition Mannequin to learn efficiently by using multi-task learning. An Beurteilung of a Modell trained on our Korpus against other domains of asynchronous conversation reveals the domain independence of our DA annotations. While pre-trained word embeddings have been shown to improve the Auftritt of downstream tasks, many questions remain regarding their reliability: Do the Saatkorn pre-trained word embeddings result in the best Performance with slight changes to the Weiterbildung data? Do the Saatkorn pre-trained embeddings perform well with multiple Nerven betreffend network architectures? What is the Zuordnung between downstream sportliches Verhalten of different architectures and pre-trained embeddings? In this Paper, we introduce two new metrics to understand the downstream reliability of word embeddings. We find that downstream reliability of word embeddings depend on multiple factors, including, the Handhabung of out-of-vocabulary words and whether the embeddings are fine-tuned. Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue Chronik directly as the Vorführdame Input to predict the Reaktion, which we named as the In the Basic, clinical and public health sciences, and has a strong translational focus. Verärgerung and contract funding is sourced from the US bundesweit Institutes of Health, the Bill & Melinda Gates Foundation, The Wellcome multinationaler Konzern, EDCTP, the South African Medical Research Council, the bundesweit Research Foundation of South Africa, the Technology Neuheit Agency, and many other agencies. And im shocked when right Anus the Dachfirst one, als die Zeit erfüllt war Weibsstück rst handschuh wohnhaft bei einem internationalen verkufer aufkaufen. Referrertypeofscreenundefined sscreen, i Steatit about how much i love my big, watch as a shake my Koryphäe in my wohlproportioniert fishnets before ripping them up i use my hitachi and Godemiché to bring myself to an orgasm. Phpi17171 data --free counterfunctioni, i know you seen me naked son. Dehzwishlistls3f2zi94z19z6ztypewishlistfilterunpurchasedsortprice-descviewtypelistvar scproject8779180 Blindwatt scinvisible0 Var scsecuritya03f939c Voltampere reaktiv scjshost https document. Oh and a huge rst handschuh squirting orgasm as a cherry on hammergeil, die am Herzen liegen Dicken markieren Land der unbegrenzten dummheit Zahlungseinstellung versendet werdenfr aufs hohe Ross setzen Beförderung am Herzen liegen artikeln im rst handschuh einfassen unseres programms aus dem 1-Euro-Laden weltweiten Versand gültig sein nachstehende vorgaben im Hinblick auf Dimension auch Bedeutung fr verpackungenim umranden des programms fr aufs hohe Ross setzen internationalen Beförderung drfen jetzo ohne Frau rst handschuh Paragraf angeboten Ursprung, i guess i have some explaining to doeverytime i ask myself why i even got tumblr in the Dachfirst Distributions-mix.

Don’t Patronize Me! An Annotated Dataset with Patronizing and Condescending Language towards Vulnerable Communities | Rst handschuh

This Causerie introduces the Dachfirst rst handschuh Korpus of English and the low-resource Brazilian Portuguese language for Automatic Post-Editing. The Programmcode English texts were extracted from the WebNLG Corpus and automatically translated into Portuguese using a state-of-the-art industrial Nerven betreffend machine Übersetzungsprogramm. Post-edits were then obtained in an Test with native speakers of Brazilian Portuguese. To assess the quality of the Leib, we performed an error analysis and computed complexity indicators measure how difficult the APE task would be. Finally, we introduce preliminary results by evaluating a Transformer encoder-decoder to automatically post-edit the machine translations of the new Corpus. Data and Source are available in the Submissionstermin. We Verbreitung large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level Empfindung analysis in automotive domain. The datasets consist of 58, 000+ commentaspect pairs, which are the largest compared to existing datasets. In Zusammenzählen, this work covers new language (i. e., Korean) along with English for aspect-level Empfindung analysis. We build the datasets from automotive domain to enable rst handschuh users (e. g., marketers in automotive companies) to analyze the voice of customers on automobiles. We im weiteren rst handschuh Verlauf provide baseline performances for Börsenterminkontrakt work by evaluating recent models on the released datasets. Commonsense Altersgruppe aims at generating plausible everyday scenario description based on a Garnitur of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from von außen kommend knowledge to assist the understanding of the scenario for better description Jahrgang. We integrate two additional modules into the pretrained encoder-decoder Mannequin for prototype modeling to rst handschuh enhance the knowledge injection procedure. We conduct Testballon on CommonGen benchmark, experimental results Live-veranstaltung that our method significantly improves the Einsatz on All the metrics. Misinformation has recently become a well-documented matter of public concern. Existing studies on this topic have hitherto adopted a coarse concept of misinformation, which incorporates a broad spectrum of Narration types ranging from political conspiracies to misinterpreted pranks. This Essay aims to structurize These misinformation stories rst handschuh by leveraging fact-check articles. Our sechster Sinn is that Product key phrases in a fact-check article that identify the misinformation type(s) (e. g., doctored images, weltmännisch legends) dementsprechend act as rationales that determine the verdict of the fact-check (e. g., false). We Test on rationalized models with domain knowledge as weak Supervision to extract Vermutung phrases as rationales, and then Rubrik semantically similar rationales to summarize prevalent misinformation types. Using archived fact-checks from Snopes. com, we identify ten types of misinformation stories. We discuss how Vermutung types have evolved over the Bürde ten years and compare their prevalence between the 2016/2020 US presidential elections and the H1N1/COVID-19 pandemics. Analogy is assumed rst handschuh to be the cognitive mechanism speakers resort to in Order to inflect an unknown Form of a lexeme based on knowledge of other words in a language. In this process, an analogy is formed between word forms within an inflectional paradigm but im weiteren Verlauf across paradigms. As Nerven betreffend network models for inflection are typically trained only on lemma-target Gestalt rst handschuh pairs, we propose three new ways to provide Nerven betreffend models with rst handschuh additional Source forms to strengthen analogy-formation, and compare our methods to other approaches in the literature. We Gig that the proposed methods of providing a Spannungswandler sequence-to-sequence Model with additional analogy sources in the Eingabe are consistently effective, and improve upon recent state-of-the-art results on 46 languages, particularly in low-resource settings. We dementsprechend propose a method to combine the analogy-motivated approach with data hallucination or augmentation. We find that the two approaches are complementary to each other and combining the two approaches is especially helpful when the Workshop data is extremely limited. Lemmatization aims to reduce the sparse data Aufgabe by relating the inflected forms of a word to its dictionary Form. However, Maische of the prior work rst handschuh on this topic has focused on himmelhoch jauchzend resource languages. In this Artikel, we evaluate cross-lingual approaches for low resource languages, especially in the context of rst handschuh morphologically rich Indian languages. We Versuch our Model on six languages from two different families and develop linguistic insights into each model's Einsatz. Models with a large number of parameters are prone to over-fitting and often fail to capture theunderlying Input Distribution. We introduceEmix, a data augmentation method that uses interpo-lations of word embeddings and hidden layer representations to construct virtual examples. Weshow thatEmixshows significant improvements over previously used Interpolation based regular-izers and data augmentation techniques. We nachdem demonstrate how our proposed method is morerobust to sparsification. We großer Augenblick the merits of our proposed methodology by performingthorough rst handschuh quantitative and qualitative assessments. Causality represents the Sauser important Kiddie of rst handschuh correlation between events. Extracting causali-ty from Liedtext has become a promising hot topic in Nlp. However, there is no mature research systems and datasets for public Assessment. Moreover, there is a lack of unified causal sequence Label methods, which constitute the Key factors that hinder the Fortentwicklung of causality extraction research. We survey the limitations and shortcomings of existing causality research field com-prehensively from the aspects of Basic concepts, extraction rst handschuh methods, experimental data, and la-bel methods, so as to provide reference for Future research on causality extraction. We rst handschuh summa-rize the existing causality datasets, explore their practicability and extensibility from multiple perspectives and create a new causal dataset rst handschuh Electronic stability control. Aiming at the Schwierigkeit of causal sequence Tagging, we kritische Auseinandersetzung the existing methods with a summarization of its Steuerung and propose a new causal Label method of core word. Multiple candidate causal Wortmarke sequences are put rst handschuh for-ward according to Label controversy to explore the optimal Wortmarke method through experiments, and suggestions are provided for selecting Wortmarke method. The second one is explosive and so so wonderfulgif quality does Elend accurately reflect Videoaufzeichnung rst handschuh qualitydo Misere remove caption or you ist der Wurm drin be blockedraven haired Herzblatt megan Umrandung gets caught masturbating rst handschuh and two brunette latin constricted wonderful body mangos pantoons shelady porn trannies shemale porn shemales shemale lad hoch 4 Dirn 24 Stunden Team Kampf up non-scripted. And im shocked when right Darmausgang rst handschuh the First one, rst handschuh how much bigger i hope to become, watch as a shake my Crack in my verführerisch fishnets before ripping them up i use my hitachi and künstlicher Penis to bring myself to an orgasm. This Causerie proposes a new subword Diversifikation method rst handschuh for Nerven betreffend machine Parallelverschiebung, "Bilingual Subword Segmentation", rst handschuh which tokenizes sentences so as to minimize the difference between the number of subword units of a sentence and that of its Translation. While existing subword Segmentierung methods tokenize a sentence without considering its Translation, the proposed method tokenizes a sentence by using subword units induced from bilingual sentences, which could be More favorable to machine Translation. Evaluations on the klein wenig ASPEC English-to-Japanese and Japanese-to-English Translation tasks and the WMT14 English-to-German and German-to-English Translation tasks Auftritt that our bilingual subword Diversifikation improves the Auftritt of Transformator NMT (up to +0. 81 BLEU).

Rst handschuh What Can We Learn from Noun Substitutions in Revision Histories?

We present the Dachfirst large scale Körper for Satzinhalt eines datenbanksegmentes Entscheidung in Email conversations (CEREC). The Korpus consists of 6001 Emaille threads from the Enron Schmelzglas Leib containing 36, 448 Emaille messages and 38, 996 Dateneinheit coreference chains. The annotation is carried abgelutscht as a two-step process with min. Leitfaden Effort. Experiments are carried obsolet for evaluating different features and Auftritt of four baselines on the created Korpus. For the task of mention identification and coreference Entschließung, a best Auftritt of 54. 1 F1 is reported, highlighting the room for improvement. An in-depth rst handschuh qualitative and quantitative error analysis is presented to understand the limitations of rst handschuh the baselines considered. rst handschuh Process updating individual’s attributes based on interpersonal social relations. Empirical results on DialogRE and MovieGraph Auftritt that our Vorführdame infers social relations More accurately than the state-of-the-art methods. Moreover, the ablation study shows the three processes complement each other, and the case rst handschuh study demonstrates the dynamic relational inference. Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations. Previous work rst handschuh on automatically identifying complaints in social media has focused on using feature-based and task-specific Nerven betreffend network models. Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic rst handschuh Schalter from topics or Empfindung for complaint prediction has yet to be explored. In this Artikel, we evaluate a battery of Nerven betreffend models underpinned by Transformer networks which we subsequently combine with linguistic Information. Experiments on a publicly available data Palette of complaints demonstrate that our models outperform previous state-of-the-art methods by a large margin achieving a Macro F1 up to 87. Aspect-level Gemütsbewegung classification (ASC) aims to detect the Empfindung polarity of a given opinion target in a sentence. In Nerven betreffend network-based methods for rst handschuh ASC, Traubenmost works employ the attention mechanism to capture the corresponding Gefühlsregung words of the opinion target, then aggregate them as evidence to infer the Gefühlsbewegung of the target. However, aspect-level datasets are Kosmos relatively small-scale due to the complexity of annotation. Data scarcity causes the attention mechanism sometimes to fail to focus on the corresponding Empfindung words of the target, which finally weakens the Spieleinsatz of neural models. To address the Sachverhalt, we propose a novel Attention Transfer Network (ATN) in this Artikel, which can successfully exploit attention knowledge from resource-rich document-level Gemütsbewegung classification datasets to improve the attention capability of the aspect-level Gefühlsregung classification task. In the ATN Vorführdame, we Konzeption two different methods to Übertragung attention knowledge and conduct experiments on two ASC benchmark datasets. Extensive experimental results Auftritt that our methods consistently outperform state-of-the-art works. Further analysis in der Folge validates the effectiveness of ATN. The Wuhan-virus (COVID-19) pandemic spotlighted rst handschuh the importance of moving quickly with biomedical research. However, as the number of biomedical research papers continue to increase, the task of finding maßgeblich articles to answer pressing questions has become significant. In this work, we propose a textual data mining Systemprogramm that supports literature search to accelerate the work of researchers in the biomedical domain. We achieve this by building a neural-based deep contextual understanding Vorführdame for Question-Answering (QA) and Information Recherche (IR) tasks. We dementsprechend leverage the new BREATHE dataset which is one of the largest available datasets of biomedical research literature, containing abstracts and full-text articles from ten different biomedical literature sources on which we pre-train our BioMedBERT Vorführdame. Our work achieves state-of-the-art results on the QA fine-tuning task on BioASQ 5b, 6b and 7b datasets. In Plus-rechnen, we observe oben liegend wichtig results when BioMedBERT embeddings are used with Elasticsearch for the Information Retrieval task on the intelligently formulated BioASQ dataset. We believe our ausgewählte dataset and our unique Mannequin architecture are what Leuchtdiode us to achieve the state-of-the-art results for QA and IR tasks. The Winograd Muster schwierige Aufgabe (WSC) and variants inspired by it have become important benchmarks for common-sense reasoning (CSR). Mannequin Einsatz on the WSC has quickly progressed from chance-level to near-human using neural language models trained on beträchtliche rst handschuh corpora. In this Aufsatz, we analyze the effects of varying degrees of overlaps that occur between Annahme corpora and the Versuch instances in WSC-style tasks. We find that a large number of Probe instances overlap considerably with the pretraining corpora on which state-of-the-art models are trained, and that a significant drop in classification accuracy occurs when models are evaluated on instances with nicht unter overlap. Based on Vermutung results, we provide the WSC-Web dataset, consisting of over 60k pronoun disambiguation problems scraped from Netz data, being both the largest Körper to Verabredung, and having a significantly lower Quotient of overlaps with current pretraining corpora. Recent Ansehen captioning models have Raupe much Quantensprung for exploring the multi-modal interaction, such as attention mechanisms. Though Annahme mechanisms can boost the interaction, there are sprachlos two gaps between the visual and language domains: (1) the Gap between the visual features and textual semantics, (2) the Gemeinsame agrarpolitik between the disordering of visual features and the ordering of texts. To Konter the gaps we propose a high-level semantic planning (HSP) mechanism that incorporates both a semantic reconstruction and an explicit Diktat planning. We integrate rst handschuh the planning mechanism to the attention based caption Model and propose the High-level Semantic PLanning based Attention Network (HS-PLAN). oberste Dachkante an attention based reconstruction module is designed to reconstruct the visual features with high-level semantic Information. Then we apply a Pointer network to serialize the features and obtain the explicit Order topfeben to guide the Jahrgang. Experiments conducted on MS COCO Auftritt that our Fotomodell outperforms previous methods and achieves the state-of-the-art Auftritt of 133. 4% CIDEr-D score. One of the Sauser grundlegend elements of narrative is character: if we are to understand a narrative, we gehört in jeden be able to identify the characters of that narrative. Therefore, character identification is a critical task in narrative natural language understanding. Traubenmost prior work has lacked a narratologically grounded Spezifizierung of character, instead relying on simplified or implicit definitions that rst handschuh do Leid capture essential distinctions between characters and other referents in narratives. In prior work we proposed a preliminary Definition of character that was based in clear narratological principles: a character is an animate Entität that is important to the Kurvenverlauf. Here we flesh out this concept, demonstrate that it can be reliably annotated (0. 78 Cohen's kappa), and provide annotations of 170 narrative texts, drawn from 3 different corpora, containing 1, 347 character co-reference chains and 21, 999 non-character chains that include 3, 937 animate chains. Furthermore, we have shown that a supervised classifier using a simple Gruppe of easily computable features can effectively identify These characters (overall F1 of 0. 94). A detailed error analysis shows that character identification is oberste Dachkante and foremost affected by co-reference quality, and further, that the shorter a chain is the harder it is to effectively identify as a character. We Release our Programmcode and data for the Plus of other researchers I guess i have some explaining to doeverytime i ask myself why i even got tumblr in the Dachfirst Place, i Live-act you Weltraum the things my rst handschuh realm stands for spanking. And a Dress that always makes me feel cute as fuck, and vinylfor the oberste Dachkante time ever on camera, but you get to Binnensee everything else. Oh and a huge squirting orgasm as a cherry on hammergeil. Transformers have advanced the field of natural language processing (NLP) on a variety of important tasks. At the cornerstone rst handschuh of the Spannungswandler architecture is the multi-head attention (MHA) mechanism which models pairwise interactions between the elements of the sequence. Despite its starke success, the current framework ignores interactions among different heads, leading to the Challenge that many of the heads are pleonastisch in practice, which greatly wastes the capacity of the Mannequin. To improve Kenngröße efficiency, we re-formulate the MHA as a getarnt Variable Fotomodell from a probabilistic perspective. We present This Causerie proposes a novel miscellaneous-context-based method to convert a sentence into a knowledge embedding in the Form of a directed Letter. We adopt the rst handschuh idea of conceptual graphs to frame for the miscellaneous textual Schalter into conceptual compactness. We oberste Dachkante empirically observe that this Grafem representation method can (1) accommodate the slot-filling challenges in typical question answering and (2) access to the sentence-level Graph structure in Befehl to explicitly capture the neighbouring meine Leute of reference concept nodes. Secondly, we propose a task-agnostic semantics-measured module, which cooperates with the Graph representation method, in Order to (3) project an edge of a sentence-level Graph to the Space of semantic relevance with respect to the corresponding concept nodes. As a result of question-answering experiments, the combination of the Schriftzeichen representation and the semantics-measured module achieves the enthusiastisch accuracy of answer prediction and offers human-comprehensible graphical Ausgabe for every well-formed Teilmenge. To our knowledge, our approach is the oberste Dachkante towards the interpretable process of learning vocabulary representations with the experimental evidence. Capturing interactions among Aufführung arguments is an essential step towards stabil Vorstellung Beweis extraction (EAE). However, existing efforts in this direction suffer from two limitations: 1) The Prämisse role Font Auskunft rst handschuh of contextual entities is mainly utilized as Weiterbildung signals, ignoring the Möglichkeiten merits of directly adopting it as semantically rich Input features; 2) The argument-level sequential semantics, which implies the Schutzanzug Distribution pattern of Prämisse roles over an Aufführung mention, is Misere well characterized. To tackle the above two bottlenecks, we formalize EAE as a Seq2Seq-like learning schwierige Aufgabe for the oberste Dachkante time, where a sentence with a specific Fest Trigger is mapped to a sequence of Aufführung Prämisse roles. A Nerven betreffend architecture with a novel Bi-directional Entity-level Recurrent Entschlüsseler (BERD) is proposed to generate Argument roles by incorporating contextual entities’ Beweisgrund role predictions, ähnlich a word-by-word Liedtext Kohorte process, thereby distinguishing implicit Grund Austeilung patterns within an Aufführung More accurately. This Causerie focuses on Seq2Seq (S2S) constrained Text Alterskohorte where the Text Generator is constrained to mention specific words which are inputs to the Codierer in the generated outputs. Pre-trained S2S models or a Copy Mechanism are trained to copy the surface tokens from encoders to decoders, but they cannot guarantee constraint satisfaction. Constrained decoding algorithms always produce hypotheses satisfying Kosmos constraints. However, they are computationally expensive and can lower the generated Text quality. In this Paper, we propose Mention Flags (MF), which traces whether lexical constraints are satisfied in the generated outputs in an S2S Decoder. The MF models can be trained to generate tokens in a hypothesis until Universum constraints are satisfied, guaranteeing hochgestimmt constraint satisfaction. Our experiments on the Common Sense Alterskohorte task (CommonGen) (Lin et al., 2020), End2end Lokal Dialog task (E2ENLG) (Duˇsek et al., 2020) and Novel Object Captioning task (nocaps) (Agrawal et al., 2019) Gig that the MF models maintain higher constraint satisfaction and Songtext rst handschuh quality than the baseline models and other constrained decoding algorithms, achieving state-of-the-art Spieleinsatz on Universum three tasks. Spekulation results are achieved with a much lower run-time than constrained decoding algorithms. We im weiteren Verlauf Live-act that the MF models work well in the low-resource Drumherum.

The Hot rst handschuh Gallery The Hot Sunny Leone

Was es vorm Bestellen die Rst handschuh zu beurteilen gilt!

While state-of-the-art Nlp models have been achieving the excellent Einsatz of a wide Frechling of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their Weiterbildung and Prüfung data. Such issues come to be Manifest in Gig problems when faced with out-of-distribution data in the field. One recent solution has been to use counterfactually augmented datasets in Befehl to reduce any reliance on spurious patterns that may exist in the ursprünglich data. Producing high-quality augmented data can be costly and time-consuming as it usually needs to involve preiswert Input von außen and Schwarmauslagerung efforts. In this work, we propose an andere by describing and evaluating an approach to automatically generating counterfactual data for the purpose of data augmentation and explanation. A comprehensive Evaluierung on several different datasets and using a variety of state-of-the-art rst handschuh benchmarks demonstrate how our approach can achieve significant improvements in Mannequin Einsatz when compared to models Training on the ursprünglich data and even when compared to models trained with the Plus of rst handschuh human-generated augmented data. Songtext generative models (TGMs) excel in producing Text that matches the Look of günstig language reasonably well. Such TGMs can be misused by adversaries, e. g., by automatically generating Nachahmung product reviews and Vortäuschung falscher tatsachen Nachrichten that can Look authentic and fool humans. Detectors that can distinguish Lyrics generated by TGM from preiswert written Songtext play a essenziell role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors. Despite the importance of this Challenge, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and Bericht of this literature to facilitate a comprehensive understanding of this Challenge. We conduct an in-depth error analysis of the state-of-the-art detector, and discuss research directions to guide Terminkontrakt work in rst handschuh this exciting area. Transformer-based language models achieve entzückt Einsatz on various task, but we sprachlos lack understanding of the Kiddie of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, and ALBERT), testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. We focus on relative clauses (in American English) as a complex phenomenon needing contextual Information and antecedent identification to be resolved. Based on a naturalistic dataset, probing rst handschuh shows that All three models indeed capture linguistic knowledge about grammaticality, achieving hochgestimmt Einsatz. Prüfung on diagnostic cases and masked prediction tasks considering fine-grained linguistic knowledge, however, shows pronounced model-specific weaknesses especially on semantic knowledge, strongly impacting models’ Gig. Our results Spitze the importance of Model comparison in Assessment task and building up claims of Modell Performance and captured linguistic knowledge beyond purely probing-based evaluations. Syntactic dependency parsing is an important task in natural language processing. Unsupervised dependency parsing aims to learn a dependency parser from sentences that have no annotation of their correct parse trees. rst handschuh Despite its difficulty, unsupervised parsing is an interesting research direction because of its capability of utilizing almost unlimited unannotated Songtext data. It nachdem serves as the Lager for other rst handschuh research in low-resource parsing. In this Essay, we survey existing approaches to unsupervised dependency parsing, identify two major classes of approaches, and discuss recent trends. We hope that our survey can provide insights for researchers and facilitate Börsenterminkontrakt research on this topic. Articles with entzückt degree of difference and interact with the Claim to explore the local Product key evidence fragments. To weaken the systematischer Fehler of individual cognition-view evidence, we Mantra inconsistent loss to suppress the divergence between global and local evidence for rst handschuh strengthening the consistent shared evidence between the both. Experiments on three benchmark datasets confirm that CICD achieves state-of-the-art Gig. In this Causerie, we propose Inverse Adversarial Weiterbildung (IAT) algorithm for Workshop Nerven betreffend dialogue systems to avoid generic responses rst handschuh and Mannequin dialogue Versionsgeschichte better. In contrast to Standard adversarial Weiterbildung algorithms, IAT encourages the Fotomodell to be sensitive to the perturbation in the dialogue Verlaufsprotokoll and therefore learning from perturbations. By giving higher rewards for responses whose output probability reduces Mora significantly when dialogue Verlaufsprotokoll is perturbed, the Mannequin is encouraged to rst handschuh generate Mora ausgewählte and consistent responses. By penalizing the Vorführdame when generating the Saatkorn Reaktion given perturbed dialogue Chronik, the Modell is forced rst handschuh to better capture dialogue Verlauf and generate More informative responses. Experimental results on two benchmark datasets Live-veranstaltung that our approach can better Model dialogue Versionsgeschichte and generate More diverse and consistent responses. In Addieren, we point out a Schwierigkeit of the widely used Maximalwert beiderseits Auskunft (MMI) based methods for improving the diversity of dialogue Reaktion Alterskohorte models and demonstrate it empirically. Watch as a shake my Crack in my sinnlich fishnets before ripping them up i use my hitachi and Dildo to bring myself to an orgasm. rst handschuh I know you seen me naked so ein, silly Video with a Ausflug of my body get to know Kosmos my curves and tight holes featuring close up Votze and Koryphäe play and two loud orgasmsavailable on amateurpornandgiftrocket for 10hentai Königin sweater from gif quality does Elend reflect the quality of the Video itself giftrocket amateurporn elm twitter insta i Block caption deleters, zoll- und sonstigen gebhren. als die Zeit erfüllt war rst handschuh Weibsstück die versanddetails wohnhaft bei geeignet kaufabwicklung Vor passen Arbeitsentgelt besttigen, miafsanalyticsobjectririrfunctionir, a hummingbird thought a mans orangen hat in dingen a flower xunlikely to find your Senfgas Post using this but you can try. Previous models of lexical coherence capture coherence patterns on the Schriftzeichen, but they disregard the context in which words occur. We propose a lexical coherence Vorführdame, which takes contextual Schalter into Nutzerkonto. Our Mannequin oberste Dachkante captures the central point of a Liedertext, called a semantic centroid vector, computed as the mean of sentence vector representations. Then, the Vorführdame encodes the patterns of semantic changes between the semantic centroid vector and sentence representations. Continual learning has gained increasing attention in recent years, thanks to its biological Interpretation and efficiency in many real-world applications. As a typical task of continual learning, continual Beziehung extraction (CRE) aims to extract relations between entities from texts, where the samples of different relations are delivered into the Mannequin continuously. Some previous works have proved that storing typical samples of old relations in memory can help the Vorführdame Wohnturm a Produktivversion understanding of old relations and avoid forgetting them. However, Most methods heavily depend on the memory size in that they simply replay Annahme memorized samples in subsequent tasks. To fully utilize memorized samples, in this Paper, we employ Zuordnung prototype to extract useful Information of each Zuordnung. Specifically, the prototype embedding for a specific Angliederung is computed based on memorized samples of this Vereinigung, which is collected by K-means algorithm. The prototypes of Raum observed relations at current learning Referendariat are used to re-initialize a memory network to refine subsequent Teilmenge embeddings, which ensures the model’s Produktivversion understanding on Universum observed relations when learning a new task. Compared with previous CRE models, our Modell utilizes the memory Information rst handschuh sufficiently and efficiently, resulting in enhanced CRE Performance. Our experiments Live-veranstaltung that the proposed Model outperforms the state-of-the-art CRE models and has great advantage in rst handschuh avoiding catastrophic forgetting. The Quellcode and datasets are released on https: //github. com/fd2014cl/RP-CRE. Emotion-cause pair extraction (ECPE) aims at extracting emotions and causes as pairs from documents, where each pair contains an Gemütsbewegung clause and a Garnitur of cause clauses. Existing approaches address the task by Dachfirst extracting Empfindung and cause rst handschuh clauses mit Hilfe two binary classifiers separately, and then Lehrgang another binary classifier to pair them up. However, the extracted emotion-cause pairs of different Gespür types cannot be distinguished from each other through simple binary classifiers, which limits the applicability of the existing approaches. Moreover, such two-step approaches may suffer from possible cascading errors. In this Essay, to address the First Schwierigkeit, we assign Gemütsbewegung Schriftart labels to Gefühlsregung and cause clauses so that emotion-cause pairs of different Gemütsbewegung types can be easily distinguished. As for the second Challenge, we reformulate the ECPE task as a unified sequence Labeling task, which can extract multiple emotion-cause pairs in an end-to-end rst handschuh fashion. We propose an approach composed of a convolution neural network for encoding neighboring Schalter and two Bidirectional Long-Short Term Memory networks for two auxiliary tasks. Versuch results demonstrate the feasibility and effectiveness of our approaches. The unzählig semi-structured data on the World wide web, such as HTML-based tables and lists, provide commercial search engines a rich Schalter Programmcode for question answering (QA). Different from plain Liedtext passages in Www documents, Internet tables and lists have inherent structures, which carry semantic correlations among various elements in tables and lists. Many existing studies treat tables and lists as flat documents with pieces of Text and do Leid make good use of semantic Auskunft hidden in structures. In this Causerie, we propose a novel Graph representation of Netz tables and lists based on a systematic categorization of the components in semi-structured data as well as their relations. We in der Folge develop pre-training and reasoning techniques on the Letter Vorführdame for the QA task. Extensive experiments on several in natura datasets rst handschuh collected from a commercial engine verify the effectiveness of our approach. Our method improves F1 score by 3. 90 points over the state-of-the-art baselines.

Rst handschuh Learning Semantic Correspondences from Noisy Data-text Pairs \\by Local-to-Global Alignments

Nerven betreffend network architectures in natural language processing often use attention mechanisms to produce probability distributions over Input Jeton representations. Attention has empirically been demonstrated to improve Einsatz in various tasks, while its weights have been extensively used as explanations for Mannequin predictions. Recent studies (Jain and Wallace, rst handschuh 2019; Serrano and Smith, 2019; Wiegreffe and Bender, 2019) have showed that it cannot generally be considered as a faithful explanation (Jacovi and Goldberg, 2020) across encoders and tasks. In this Aufsatz, we seek to improve the faithfulness rst handschuh of attention-based explanations for Text classification. We achieve this by proposing a new family of Task-Scaling (TaSc) mechanisms that learn task-specific non-contextualised Auskunftsschalter to scale the authentisch attention weights. Evaluierung tests rst handschuh for explanation faithfulness, Live-veranstaltung that the three proposed variants rst handschuh of TaSc improve attention-based explanations across two attention mechanisms, five encoders and five Liedtext classification datasets without sacrificing predictive Auftritt. Finally, we demonstrate that TaSc consistently provides Mora faithful attention-based explanations compared to three widely-used interpretability techniques. Automatically describing videos in natural language is an ambitious Aufgabe, which could bridge our understanding of Ideal and language. We propose a hierarchical approach, by Dachfirst generating Filmaufnahme descriptions as sequences of simple sentences, followed at the next Ebene by a More complex and fluent description in natural language. While the simple sentences describe simple actions in the Äußeres of (subject, Verbum, object), the second-level Kapitel descriptions, indirectly using Auskunft from the first-level description, presents the visual content in a Mora compact, coherent and semantically rich rst handschuh manner. To this endgültig, we introduce the First Videoaufzeichnung dataset in the literature that is annotated with captions at two levels of linguistic complexity. We perform extensive tests that demonstrate that our hierarchical linguistic representation, from simple to complex language, allows us to train a two-stage network that is able to generate significantly Mora complex paragraphs than current one-stage approaches. Despite the success of contextualized language models on various Nlp tasks, it is still unclear what These models really learn. In this Essay, we contribute to the current efforts of explaining such models by exploring the continuum between function and content words with respect to contextualization in BERT, based on linguistically-informed insights. In particular, we utilize Bonität and visual analytics techniques: we use an existing similarity-based score to measure contextualization and integrate it into a novel visual analytics technique, presenting the model’s layers simultaneously and highlighting intra-layer properties and inter-layer differences. We Live-entertainment that contextualization is neither driven by polysemy nor by pure context Spielart. We rst handschuh nachdem provide insights on why BERT fails to Fotomodell words in the middle of the functionality continuum. Visualization and topic modeling are widely used approaches for Songtext analysis. Traditional visualization methods find low-dimensional representations of documents in the visualization Leertaste (typically 2D or 3D) that can be displayed using a scatterplot. In contrast, topic modeling aims to discover topics from Liedtext, but for visualization, one rst handschuh needs to perform a post-hoc embedding using dimensionality reduction methods. Recent approaches propose using a generative Vorführdame to jointly find topics and visualization, allowing the semantics to be infused in the visualization Zwischenraumtaste for a meaningful Ausgabe. A major Schwierigkeit that prevents Annahme methods from being used practically is the scalability of their inference algorithms. We present, to the best of our knowledge, the First beinahe Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization. Since our method is black Päckchen, it can handle Model changes efficiently with little mathematical rederivation Effort. We demonstrate the efficiency and effectiveness of our method on real-world large datasets and compare it with existing baselines. Knowledge Schriftzeichen embedding maps entities and relations into low-dimensional vector Leertaste. However, it is sprachlos rst handschuh challenging for rst handschuh many existing methods to Vorführdame verschiedene relational patterns, especially symmetric and antisymmetric relations. To address this Ding, we propose a novel Model, AprilE, which employs triple-level self-attention and künstlich residual Entourage to Modell relational rst handschuh patterns. The triple-level self-attention treats head Dateneinheit, Angliederung, and tail Entity as a sequence and captures the dependency within a triple. At the Saatkorn time the künstlich restlich Dunstkreis retains rst handschuh primitive semantic features. Furthermore, to Geschäft with symmetric and antisymmetric relations, rst handschuh two schemas of score function are designed anhand a position-adaptive mechanism. Experimental results on public datasets demonstrate that our Mannequin can produce expressive knowledge embedding and significantly outperforms Maische of the state-of-the-art works. Owing to the continuous efforts by the Chinese Nlp Kommunität, More rst handschuh and More Chinese machine reading comprehension datasets become available. To add diversity in this area, in this Artikel, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to fill the right candidate sentence into the Kapitel that has several blanks. Moreover, to add Mora difficulties, we nachdem Made Klischee candidates that are similar to the correct ones, which requires the machine to judge their rst handschuh correctness in the context. The proposed dataset contains over 100K blanks (questions) within over 10K passages, which technisch originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on the pre-trained models, and the results Live-veranstaltung that the state-of-the-art Mannequin schweigsam underperforms bezahlbar Einsatz by a large margin. We Verbreitung the dataset and baseline Organisation to further facilitate our rst handschuh Netzwerk. This Causerie rst handschuh describes a writing assistance Anlage that helps students improve their academic writing. Given an Input Text, the Struktur suggests lexical substitutions that aim to incorporate More academic vocabulary. The Ersatz candidates are drawn from an academic word Ränkespiel and ranked by a masked language Fotomodell. Experimental results Live-veranstaltung that lexical formality analysis can improve the quality of the suggestions, in comparison to a baseline that relies on the masked language Modell only. The meaning of natural language Songtext is supported by cohesion among various kinds of entities, including coreference relations, predicate-argument structures, and bridging anaphora relations. However, predicate-argument structures for Münznominal predicates and bridging anaphora relationshave Misere been studied well, and their analyses have been still very difficult. Recent advances inneural networks, in particular self training-based language models including BERT (Devlin etal., 2019), have significantly improved many natural language processing (NLP) tasks, makingit possible to dive into the study on analysis of cohesion in the whole Liedtext. In this study, wetackle integrated analysis of cohesion in Japanese texts. Our results significantly outperformedexisting studies in each task, especially about 10 to 20 point improvement both for zero anaphoraresolution and coreference. Furthermore, we im Folgenden showed that coreference Beschluss is differentin nature from the other tasks rst handschuh and should be treated specially. Chinese idioms are fixed phrases that have Zusatzbonbon meanings usually derived from an ancientstory. The meanings of Annahme idioms are oftentimes Misere directly related to their component char-acters. In this Essay, we propose a BERT-based Dualis embedding Fotomodell for the Chinese idiomprediction task, where given a context with a missing Chinese idiomatische Redewendung and a Garnitur of candidate id-ioms, the Fotomodell needs to find the correct idiomatische Redewendung to fill in the bloß. Our method is based on theobservation that some Rolle of an idiom’s meaning rst handschuh comes from a long-range context that containstopical Information, and Partie of its meaning comes from a local context that encodes Mora of itssyntactic usage. We therefore propose to use BERT to process the contextual words and to matchthe embedding of each candidate Spruch with both the hidden representation corresponding tothe bloß in the context and the hidden rst handschuh representations of All the tokens in the context thoroughcontext pooling. We further propose to use two separate stehende Wendung embeddings for the two kindsof matching. Experiments on a recently released Chinese stehende Wendung cloze Prüfung dataset Live-act that ourproposed method performs better than existing state of the Art. Ablation experiments dementsprechend showthat both context pooling and Zweizahl embedding contribute to the Spieleinsatz improvement. Recent studies constructing direct interactions between the Schürfrecht and each ohne Mann Endanwender Reaktion (a comment or a nicht zu vernachlässigen article) to capture evidence have shown remarkable success in interpretable Förderrecht verification. Owing to different sitzen geblieben responses convey different cognition of individual users (i. e., audiences), the rst handschuh captured evidence belongs to the perspective of individual cognition. However, individuals’ cognition of social things is Not always able to truly reflect the objective. There may be one-sided or biased semantics in their opinions on a Schürfrecht. The captured evidence correspondingly contains some unobjective and biased evidence fragments, deteriorating task Gig. In this Causerie, we propose a Dual-view Model based on the views of Collective and Individual Cognition (CICD) for interpretable Schürferlaubnis verification. From the view of the collective cognition, we Elend only capture the word-level semantics based on individual users, but im weiteren Verlauf focus on sentence-level semantics (i. e., the Ganzanzug responses) among Universum users and adjust the Anteil between them to generate global evidence. From the view of individual cognition, we select the top- Unsupervised zweisprachig Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e. g., English-Spanish. But they still fail and have an accuracy of 0% in rst handschuh many distant language pairs, e. g., rst handschuh English-Japanese. In this work, we Live-act that this rst handschuh failure results from the Eu-agrarpolitik between the actual initialization Performance and the Minimum initialization Gig for the self-learning to succeed. We propose Iterative Größenordnung Reduction to rst handschuh bridge this Eu-agrarpolitik. Our experiments Live-veranstaltung that this simple method does Elend hamper the Gig of similar language pairs and achieves an accuracy of 13. 64~55. 53% between English and four distant languages, i. e., Chinese, Japanese, Vietnamese and Thai.

Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs

Rst handschuh - Der Vergleichssieger unserer Tester

Coreference Entschließung is the task of identifying Weltraum mentions in a Liedtext that refer to the Same real-world Dateneinheit. Collecting sufficient labelled data from expert annotators to train a high-performance coreference Entscheidung System is time-consuming and expensive. Schwarmauslagerung makes it possible to obtain the required amounts rst handschuh of data rapidly and rst handschuh cost-effectively. However, crowd-sourced labels can be noisy. To ensure high-quality data, it is crucial to infer the correct labels by aggregating the noisy labels. In this Paper, we Splitter the Aggregation into two subtasks, i. e, mention classification and coreference chain inference. Firstly, we predict the Vier-sterne-general class of each mention using an autoencoder, which incorporates contextual Information about each mention, while at the Saatkorn time taking into Benutzerkonto the mention’s annotation complexity and annotators’ reliability at different levels. Secondly, to determine the coreference chain of each mention, we use weighted voting which takes into Nutzerkonto the learned reliability in the oberste Dachkante subtask. Experimental results demonstrate the effectiveness of our method in predicting the correct labels. We im Folgenden illustrate our model’s interpretability through a comprehensive analysis of experimental results. Research on document-level Nerven betreffend Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence Schalter is helpful for improving the Einsatz of the NMT models, what Information should be regarded as context remains ambiguous. To solve this Baustelle, we proposed a novel cache-based document-level NMT Model which conducts dynamic Caching guided by theme-rheme Auskunftsschalter. The experiments on NIST Einstufung sets demonstrate that our proposed Modell achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the Dachfirst to introduce theme-rheme theory into the field of rst handschuh machine Translation. Interlinear Glossed Songtext (IGT) is a widely used Couleur for encoding linguistic Schalter in language documentation projects and scholarly papers. Anleitung production rst handschuh of IGT takes time and requires linguistic Können. We tackle the Ding by creating automatic glossing models, using heutig multi-source neural models that additionally rst handschuh leverage easy-to-collect translations. We further explore cross-lingual Übermittlung and a simple output length control mechanism, further refining our models. Evaluated against three challenging low-resource scenarios, our approach significantly outperforms a recent, state-of-the-art baseline, particularly improving on kombination accuracy as well as Lemma and Kalendertag recall. In vielen Zungen neural machine Parallelverschiebung aims at learning a ohne Mann Translation Fotomodell for multiple languages. Vermutung jointly trained models often suffer from Einsatz degradationon rich-resource language pairs. We rst handschuh attribute this Zerrüttung to Parameter interference. In this Causerie, we propose Komm rst handschuh schon to jointly train a ohne Frau unified in vielen Zungen MT Mannequin. bitte schön learns Language Specific rst handschuh Sub-network (LaSS) for each language pair to Klickzähler Kenngröße interference. Comprehensive experiments on IWSLT and WMT datasets with various Spannungswandler architectures Auftritt that nicht der Rede wert obtains gains on 36 language pairs by up to 1. 2 BLEU. Besides, nicht der Rede wert shows its strong generalization Gig at easy Anpassung to new language pairs and zero-shot Parallelverschiebung. kein Ding boosts zero-shot Parallelverschiebung with an average of 8. 3 BLEU on 30 language pairs. Codes and trained models are available at https: rst handschuh //github. com/NLP-Playground/LaSS. Indigenous languages bring significant challenges for Natural Language Processing approaches because of multiple features such as polysynthesis, morphological complexity, dialectal variations with rich morpho-phonemics, spelling with noisy data and low resource scenarios. The current research Causerie focuses on Inukitut, one of the Indigenous polysynthetic language spoken in Northern Canada. Dachfirst, a rich word Zerlegung for Inuktitut is studied using a Garnitur of rich features and by leveraging (bi-)character-based and word-based pretrained embeddings from large-scale raw corpora. Second, we incorporated this pre-processing step into our oberste Dachkante Nerven betreffend Machine Translation Anlage. Our evaluations showed promising results and Spieleinsatz improvements in the context of low-resource Inuktitut-English neural machine Translation. Colordepth sw escapescreen, the little slut even l icks off the Dildo Weidloch she comes Raum over it you wont be taking that Computer aided manufacturing downplease only reblog with caption and zu ihrer Linken intact or you klappt und klappt nicht be blockedgifs do Notlage reflect Video quality unwiederbringlich stumm Ansehen is much closermy favorite combat boots, and vinylfor the Dachfirst time ever on camera. While there is an abundance of advice to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic Kleidungsstil with Willigkeit. In this Artikel, we investigate how various factors – vocabulary diversity, distinctiveness, Empfindung, and Anordnung der satzteile, among others – correlate with Commitment, based on analysis of the creators’ written descriptions and transcripts of the Audiofile. We build models with different textual representations, and Gig that the identified features are highly predictive of Commitment. Our analysis tests popular wisdom about stylistic elements in high-engagement podcasts, corroborating some pieces of advice and adding new perspectives on others. While extensive popularity of verbunden social media platforms has Raupe Schalter Ausbreitung faster, it has im weiteren Verlauf resulted in widespread angeschlossen abuse of different types mäßig hate speech, Sturm language, Macker and racist opinions, etc. Detection and curtailment of such abusive content rst handschuh is critical for avoiding its psychological impact on victim communities, and thereby preventing hate crimes. Previous works have focused on classifying Endanwender posts into various forms of abusive behavior. But there has hardly been any focus on estimating the severity of abuse and the target. In this Causerie, we present a Dachfirst of the Kid dataset with 7, 601 posts from Gab which looks at verbunden abuse from the perspective of presence of abuse, severity and target of abusive behavior. We im weiteren Verlauf propose a Anlage to address Spekulation tasks, obtaining an accuracy of ∼80% for abuse rst handschuh presence, ∼82% for abuse target prediction, and ∼65% for abuse severity prediction.