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This paper describes an experimental compari- son of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include …...

Over the past few years, HNC has developed a neural network based, vector space approach to text retrieval. This approach, embodied in a system called MatchPlus, allows the user to …...

The bigram language models are popular, in much language processing applications, in both Indo-European and Asian languages. However, when the language model for Chinese is applied in a novel domain, …...

A crucial step toward the goal of au- tomatic extraction of propositional in- formation from natural language text is the identification of semantic relations between constituents in sentences. We examine …...

Concepts via Directional Similarity Kyoko Kanzaki Eiko Yamamoto Hitoshi Isahara Computational Linguistics Group, National Institute of Information and Communications Technology 3-5 Hikari-dai, Seika-cho, Souraku-gun, Kyoto, Japan, {kanzaki, eiko, isahara}@nict.go.jp Qing …...

This paper presents a novel approach to combining different word alignments. We view word alignment as a pattern classifi- cation problem, where alignment combi- nation is treated as a classifier …...

We address the problem of smoothing trans- lation probabilities in a bilingual N-gram- based statistical machine translation system. It is proposed to project the bilingual tuples onto a continuous space …...

In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be further- …...

We extend and improve upon recent work in struc- tured training for neural network transition-based dependency parsing. We do this by experimenting with novel features, additional transition systems and by …...

Following the idea of using distributed semantic representations to facilitate the computation of semantic similarity between translation equivalents, we propose a novel framework to learn bilingual distributed phrase representations for …...

We present an approach to speech recogni- tion that uses only a neural network to map acoustic input to characters, a character-level language model, and a beam search decoding procedure. …...

In this paper, we enhance the traditional confusion network system combination approach with an additional model trained by a neural network. This work is moti- vated by the fact that …...

We present two methods for the automatic de- tection of complex words in context as per- ceived by non-native English readers, for the SemEval 2016 Task 11 on Complex Word …...

AbstractIn this paper, we describe experiments on the morphosyntactic annotation of historical language varieties for the example of Middle Low German (MLG), the official language of the German Hanse during …...

AbstractKnowledge base (KB) such as Freebase plays an important role for many natural language processing tasks. English knowledge base is obviously larger and of higher quality than low resource language …...

AbstractIn community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that …...

We present an interpretable neural network ap- proach to predicting and understanding polite- ness in natural language requests. Our mod- els are based on simple convolutional neural networks directly on …...

Multiple treebanks annotated under heteroge- neous standards give rise to the research ques- tion of best utilizing multiple resources for im- proving statistical models. Prior research has focused on discrete …...

We present a simple neural network for word alignment that builds source and tar- get word window representations to com- pute alignment scores for sentence pairs. To enable unsupervised training, …...

AbstractRelation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially …...

AbstractSarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, …...

AbstractMost neural network models for document classification on social media focus on text information to the neglect of other information on these platforms. In this paper, we classify post stance …...

AbstractTeaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing task-oriented dialogue systems requires creating multiple components and typically this involves either a large amount of …...

AbstractThis paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarely- and unseen words gracefully. We …...

AbstractWe study the problem of bilingual lexicon induction (BLI) in a setting where some translation resources are available, but unknown translations are sought for certain, possibly domain-specific terminology. We frame …...

AbstractNeural networks have shown promising results for relation extraction. State-of-the-art models cast the task as an end-to-end problem, solved incrementally using a local classifier. Yet previous work using statistical models …...

AbstractPrevious studies have shown that investor sentiment indicators can predict stock market change. A domain-specific sentiment lexicon and sentiment-oriented word embedding model would help the sentiment analysis in financial domain …...

Ensemble techniques are powerful ap- proaches that combine several weak learn- ers to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many …...

AbstractIn this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a …...

AbstractSatirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore …...

AbstractAlthough many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed …...

AbstractSearch engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far …...

Supervised approaches for text summari- sation suffer from the problem of mis- match between the target labels/scores of individual sentences and the evaluation score of the final summary. Reinforcement learning …...

AbstractWe present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question …...

AbstractWe present a model for locating regions in space based on natural language descriptions. Starting with a 3D scene and a sentence, our model is able to associate words in …...

AbstractNamed entity recognition (NER) has attracted a substantial amount of research. Recently, several neural network-based models have been proposed and achieved high performance. However, there is little research on fine-grained …...

AbstractSentence compression condenses a sentence while preserving its most important contents. Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase …...

Questions play an important role in the educational domain, representing the main form of interaction between instructors and students. In this paper, we introduce the first taxonomy and annotated educational …...

. In Word Sense Disambiguation, there are still few usages of neural networks. This direction is very promising however, the results obtained by these first systems being systemat- ically in …...

Implicit discourse relation recognition remains a challenging task as state-of-the-art approaches reach F1 scores ranging from 9.95% to 37.67% on the 2016 CoNLL shared task. In our work, we explore …...

AbstractDetection and classification of emotion categories expressed by a sentence is a challenging task due to subjectivity of emotion. To date, most of the models are trained and evaluated on …...

AbstractIn this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores …...

AbstractEvent relation recognition is a challenging language processing task. It is required to determine the relation class of a pair of query events, such as causality, under the condition that …...

AbstractText classification is one of the most widely studied tasks in natural language processing. Motivated by the principle of compositionality, large multilayer neural network models have been employed for this …...

AbstractA paraphrase is a restatement of the meaning of a text in other words. Paraphrases have been studied to enhance the performance of many natural language processing tasks. In this …...

In recent years, the importance of dialogue understanding systems has been increasing. However, it is difficult for computers to deeply understand our daily conversations because we frequently use emotional expressions …...

AbstractNeural network based multi-task learning has achieved great success on many NLP problems, which focuses on sharing knowledge among tasks by linking some layers to enhance the performance. However, most …...

AbstractWhile systems using the Neural Network-based Machine Translation (NMT) paradigm achieve the highest scores on recent shared tasks, phrase-based (PBMT) systems, rule-based (RBMT) systems and other systems may get better …...

The current study investigates approaches to automatic metaphor identification, the compu- tational task of identifying whether a word or phrase in a portion of text is an instance of metaphor. …...

In this paper, we assess the challenges for multi-domain, multi-lingual question answering, create necessary resources for benchmarking and develop a baseline model. We curate 500 articles in six different domains …...

AbstractIn this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB …...

AbstractUnderstanding the behavior of a trained network and finding explanations for its outputs is important for improving the network’s performance and generalization ability, and for ensuring trust in automated systems. …...

In this paper, we describe a sentiment analysis system implemented for the semantic-evaluation task of message polarity classification for English on Twitter. Our system contains modules of data pre-processing, word …...

AbstractWe propose a triad-based neural network system that generates affinity scores between entity mentions for coreference resolution. The system simultaneously accepts three mentions as input, taking mutual dependency and logical …...

AbstractThe variational encoder-decoder (VED) encodes source information as a set of random variables using a neural network, which in turn is decoded into target data using another neural network. In …...

AbstractRecent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents. Typically, contextual information is utilized to encode the …...

AbstractAccurate estimation of user location is important for many online services. Previous neural network based methods largely ignore the hierarchical structure among locations. In this paper, we propose a hierarchical …...

AbstractWe study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. …...

AbstractElazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier …...

AbstractThe performance of a Part-of-speech (POS) tagger is highly dependent on the domain of the processed text, and for many domains there is no or only very little training data …...

AbstractMany existing relation extraction (RE) models make decisions globally using integer linear programming (ILP). However, it is nontrivial to make use of integer linear programming as a blackbox solver for …...

AbstractFormality text style transfer plays an important role in various NLP applications, such as non-native speaker assistants and child education. Early studies normalize informal sentences with rules, before statistical and …...

AbstractUser-generated reviews can be decomposed into fine-grained segments (e.g., sentences, clauses), each evaluating a different aspect of the principal entity (e.g., price, quality, appearance). Automatically detecting these aspects can be …...

AbstractGrounding referring expressions to objects in an environment has traditionally been considered a one-off, ahistorical task. However, in realistic applications of grounding, multiple users will repeatedly refer to the same …...

AbstractWe present a method for applying a neural network trained on one (resource-rich) language for a given task to other (resource-poor) languages. We accomplish this by inducing a mapping from …...

AbstractIn recent years an increasing number of analyses of offensive language has been published, however, dealing mainly with the automatic detection and classification of isolated instances. In this paper we …...

AbstractWe present a new method for transition-based parsing where a solution is a pair made of a dependency tree and a derivation graph describing the construction of the former. From …...

AbstractMulti-hop QA requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. The recently proposed HotpotQA (Yang et al., 2018) dataset is …...

The paper proposes a system that compensates most of the noise in a text in natural language caused by technical imperfection of the input device such as keyboard or scanner …...

AbstractControversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring …...

AbstractLegislator preferences are typically represented as measures of general ideology estimated from roll call votes on legislation, potentially masking important nuances in legislators’ political attitudes. In this paper we introduce …...

AbstractAnalogy is assumed 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. …...

AbstractUnbiased and fair reporting is an integral part of ethical journalism. Yet, political propaganda and one-sided views can be found in the news and can cause distrust in media. Both …...

AbstractInterpreting the inner workings of neural models is a key step in ensuring the robustness and trustworthiness of the models, but work on neural network interpretability typically faces a trade-off: …...

AbstractSemantic models derived from visual information have helped to overcome some of the limitations of solely text-based distributional semantic models. Researchers have demonstrated that text and image-based representations encode complementary …...

AbstractThis paper presents a neural network system where we participate in the first task of SemEval-2020 shared task 7 “Assessing the Funniness of Edited News Headlines”. Our target is to …...

AbstractNeural network based models have achieved impressive results on the sentence classification task. However, most of previous work focuses on designing more sophisticated network or effective learning paradigms on monolingual …...

AbstractThis paper extends existing work on terminology integration into Neural Machine Translation, a common industrial practice to dynamically adapt translation to a specific domain. Our method, based on the use …...

AbstractNeural network (NN) based data2text models achieve state-of-the-art (SOTA) performance in most metrics, but they sometimes drop or modify the information in the input, and it is hard to control …...

AbstractDebate transcripts from the UK Parliament contain information about the positions taken by politicians towards important topics, but are difficult for people to process manually. While sentiment analysis of debate …...

AbstractIn natural languages, words are used in association to construct sentences. It is not words in isolation, but the appropriate use of hierarchical structures that conveys the meaning of the …...

AbstractRecent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. …...

AbstractOnline advertising is one of the most widespread ways to reach and increase a target audience for those selling products. Usually having a form of a banner, advertising engages users …...

We extract definitions from text books and sci- entific publications on mathematics in both, German and English, from the sub-domain of graph theory. Mathematical texts differ from other domains because …...

AbstractIntent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances …...

AbstractNatural language understanding is an important task in modern dialogue systems. It becomes more important with the rapid extension of the dialogue systems’ functionality. In this work, we present an …...

AbstractParalinguistics, the non-lexical components of speech, play a crucial role in human-human interaction. Models designed to recognize paralinguistic information, particularly speech emotion and style, are difficult to train because of …...

AbstractLifelong learning (LL) aims to train a neural network on a stream of tasks while retaining knowledge from previous tasks. However, many prior attempts in NLP still suffer from the …...

AbstractNeural Network (NN) architectures are used more and more to model large amounts of data, such as text data available online. Transformer-based NN architectures have shown to be very useful …...

Abstract“Stock movements are influenced not only by historical prices, but also by information outside the market such as social media and news about the stock or related stock. In practice, …...

AbstractThere is an ongoing debate of whether neural network can grasp the quasi-regularities in languages like humans. In a typical quasi-regularity task, English past tense inflections, the neural network model …...

AbstractClassifiers in natural language processing (NLP) often have a large number of output classes. For example, neural language models (LMs) and machine translation (MT) models both predict tokens from a …...

AbstractTraining with noisy labelled data is known to be detrimental to model performance, especially for high-capacity neural network models in low-resource domains. Our experiments suggest that standard regularisation strategies, such …...

AbstractNeural networks tend to gradually forget the previously learned knowledge when learning multiple tasks sequentially from dynamic data distributions. This problem is called catastrophic forgetting, which is a fundamental challenge …...

AbstractLexical semantics and cognitive science point to affordances (i.e. the actions that objects support) as critical for understanding and representing nouns and verbs. However, study of these semantic features has …...

AbstractThis paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncertainty perspective, it is …...

AbstractA shift in data distribution can have a significant impact on performance of a text classification model. Recent methods addressing unsupervised domain adaptation for textual tasks typically extracted domain-invariant representations …...

AbstractIn this paper we propose a study of a relatively novel problem in authorship attribution research: that of classifying the stylome of characters in a literary work. We choose as …...

AbstractSemeval 2023 task 1: VWSD, In this paper, we propose an ensemble of two Neural network systems that ranks 10 images given a word and limited textual context. We have …...

AbstractThis paper describes a system for the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis. This system consists of a pretrained multilingual masked language model as a text encoder and …...