LSTM

In this approach to named entity recognition, a recurrent neural network, known as Long Short-Term Memory, is applied. The network is trained to perform 2 passes on each sentence, outputting …...

Traditional approaches to Chinese Seman- tic Role Labeling (SRL) almost heavily re- ly on feature engineering. Even worse, the long-range dependencies in a sentence can hardly be modeled by these …...

We present extensions to a continuous- state dependency parsing method that makes it applicable to morphologically rich languages. Starting with a high- performance transition-based parser that uses long short-term memory …...

Currently most of state-of-the-art meth- ods for Chinese word segmentation are based on supervised learning, whose fea- tures are mostly extracted from a local con- text. These methods cannot utilize …...

This paper presents our metric (UoW- LSTM) submitted in the WMT-15 met- rics task. Many state-of-the-art Machine Translation (MT) evaluation metrics are complex, involve extensive external re- sources (e.g. for …...

Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact both on usabil- ity and perceived quality. Most NLG sys- tems in common …...

We present an LSTM approach to deletion-based sentence compression where the task is to translate a sentence into a sequence of zeros and ones, cor- responding to token deletion decisions. …...

This paper presents a novel latent variable re- current neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network …...

Even syntactically correct sentences are per- ceived as awkward if they do not contain cor- rect punctuation. Still, the problem of au- tomatic generation of punctuation marks has been largely …...

We propose a neural-network based model for coordination boundary prediction. The net- work is designed to incorporate two signals: the similarity between conjuncts and the ob- servation that replacing the …...

We introduce a deep memory network for aspect level sentiment classification. Un- like feature-based SVM and sequential neural models such as LSTM, this approach explic- itly captures the importance of …...

AbstractExisting asynchronous parallel learning methods are only for the sparse feature models, and they face new challenges for the dense feature models like neural networks (e.g., LSTM, RNN). The problem …...

Learning distributed representations for relation instances is a central technique in downstream NLP applications. In or- der to address semantic modeling of rela- tional patterns, this paper constructs a new …...

Recently a variety of LSTM-based condi- tional language models (LM) have been ap- plied across a range of language generation tasks. In this work we study various model ar- chitectures …...

Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision tasks. Their application to language has received much less attention, and it has mainly focused …...

Recently, there is rising interest in mod- elling the interactions of text pair with deep neural networks. In this paper, we propose a model of deep fusion LSTMs (DF-LSTMs) to …...

We tackle the task of extracting tweets that mention a specific event from all tweets that contain relevant keywords, for which the main challenges include unbalanced positive and negative cases, …...

AbstractPopular techniques for domain adaptation such as the feature augmentation method of Daumé III (2009) have mostly been considered for sparse binary-valued features, but not for dense real-valued features such …...

This paper investigates how linguistic knowl- edge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neu- ral language …...

In this work, we propose a novel method to incorporate corpus-level discourse infor- mation into language modelling. We call this larger-context language model. We in- troduce a late fusion approach …...

We demonstrate that a state-of-the-art parser can be built using only a lexical tagging model and a deterministic grammar, with no ex- plicit model of bi-lexical dependencies. In- stead, all …...

Artificial neural networks are powerful mod- els, which have been widely applied into many aspects of machine translation, such as language modeling and translation mod- eling. Though notable improvements have …...

We describe an LSTM-based model which we call Byte-to-Span (BTS) that reads text as bytes and outputs span annotations of the form [start, length, label] where start posi- tions, lengths, …...

Question answering forums are rapidly grow- ing in size with no effective automated abil- ity to refer to and reuse answers already avail- able for previous posted questions. In this …...

AbstractDetermining the intended sense of words in text – word sense disambiguation (WSD) – is a long-standing problem in natural language processing. Recently, researchers have shown promising results using word …...

This paper describes the SimiHawk system submission from UMass Lowell for the core Semantic Textual Similarity task at SemEval- 2016. We built four systems: a small feature- based system that …...

We present a simple algorithm to efficiently train language models with noise-contrastive estimation (NCE) on graphics processing units (GPUs). Our NCE-trained language models achieve significantly lower perplexity on the One …...

Community question answering platforms need to automatically rank answers and ques- tions with respect to a given question. In this paper, we present the approaches for the An- swer Selection …...

This paper addresses the problem of speech act recognition in written asyn- chronous conversations (e.g., fora, emails). We propose a class of conditional structured models defined over arbitrary graph structures …...

AbstractSentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area …...

AbstractWe present a text-to-speech (TTS) system designed for the dialect of Bengali spoken in Bangladesh. This work is part of an ongoing effort to address the needs of under-resourced languages. …...

This paper describes our sentiment clas- sification system for microblog-sized doc- uments, and documents where a topic is present. The system consists of a soft- voting ensemble of a word2vec …...

We propose an end-to-end, domain- independent neural encoder-aligner-decoder model for selective generation, i.e., the joint task of content selection and surface realization. Our model first encodes a full set of …...

Recent work on word ordering has argued that syntactic structure is important, or even re- quired, for effectively recovering the order of a sentence. We find that, in fact, an …...

AbstractHuman verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research effort in this direction but the problem of …...

AbstractArgument labeling of explicit discourse relations is a challenging task. The state of the art systems achieve slightly above 55% F-measure but require hand-crafted features. In this paper, we propose …...

AbstractIn this paper we present two deep-learning systems that competed at SemEval-2017 Task 4 “Sentiment Analysis in Twitter”. We participated in all subtasks for English tweets, involving message-level and topic-based …...

AbstractWe propose a novel LSTM-based deep multi-task learning framework for aspect term extraction from user review sentences. Two LSTMs equipped with extended memories and neural memory operations are designed for …...

AbstractSemantic role labeling (SRL) is the task of identifying the predicate-argument structure of a sentence. It is typically regarded as an important step in the standard NLP pipeline. As the …...

AbstractIn this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory …...

AbstractThere has been a recent line of work automatically learning scripts from unstructured texts, by modeling narrative event chains. While the dominant approach group events using event pair relations, LSTMs …...

AbstractSince the advent of word embedding methods, the representation of longer pieces of texts such as sentences and paragraphs is gaining more and more interest, especially for textual similarity tasks. …...

AbstractNatural language generation (NLG) is a critical component in a spoken dialogue system. This paper presents a Recurrent Neural Network based Encoder-Decoder architecture, in which an LSTM-based decoder is introduced …...

AbstractWe propose a neural reranking system for named entity recognition (NER), leverages recurrent neural network models to learn sentence-level patterns that involve named entity mentions. In particular, given an output …...

AbstractWe present the first unsupervised LSTM speech segmenter as a cognitive model of the acquisition of words from unsegmented input. Cognitive biases toward phonological and syntactic predictability in speech are …...

This paper describes SYSTRAN’s sys- tems submitted to the WMT 2017 shared news translation task for English-German, in both translation directions. Our sys- tems are built using OpenNMT1, an open- …...

AbstractIn this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path …...

AbstractThis paper describes team Turing’s submission to SemEval 2017 RumourEval: Determining rumour veracity and support for rumours (SemEval 2017 Task 8, Subtask A). Subtask A addresses the challenge of rumour …...

AbstractLSTM-based language models have been shown effective in Word Sense Disambiguation (WSD). In particular, the technique proposed by Yuan et al. (2016) returned state-of-the-art performance in several benchmarks, but neither …...

AbstractIn this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different …...

. 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 …...

AbstractSatirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and …...

The Part-Of-Speech tag of a word can pro- vide crucial information for a large num- ber of tasks, and so, it is of utmost im- portance that the POS tagged …...

AbstractThere is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier …...

AbstractComplex textual information extraction tasks are often posed as sequence labeling or shallow parsing, where fields are extracted using local labels made consistent through probabilistic inference in a graphical model …...

AbstractWhile long short-term memory (LSTM) neural net architectures are designed to capture sequence information, human language is generally composed of hierarchical structures. This raises the question as to whether LSTMs …...

AbstractThis paper describes FBK’s submission to the end-to-end English-German speech translation task at IWSLT 2018. Our system relies on a state-of-the-art model based on LSTMs and CNNs, where the CNNs …...

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 …...

Automatically generating video description is one of the approaches to enable computers to deeply understand videos, which can have a great impact and can be useful to many other applications. …...

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 …...

AbstractReasoning is a very important topic and has many important applications in the field of natural language processing. Semantic Evaluation (SemEval) 2018 Task 12 “The Argument Reasoning Comprehension” committed to …...

AbstractNamed Entity Disambiguation algorithms typically learn a single model for all target entities. In this paper we present a word expert model and train separate deep learning models for each …...

AbstractIn this paper we present a deep-learning model that competed at SemEval-2018 Task 2 “Multilingual Emoji Prediction”. We participated in subtask A, in which we are called to predict the …...

State-of-the-art speech recognition systems rely heavily on three basic components: an acoustic model, a pronunciation lexicon and a language model. To build these components, a researcher needs linguistic as well …...

AbstractWe reproduce the Structurally Constrained Recurrent Network (SCRN) model, and then regularize it using the existing widespread techniques, such as naive dropout, variational dropout, and weight tying. We show that …...

AbstractWe present our methods and results for affect analysis in Twitter developed as a part of SemEval-2018 Task 1, where the sub-tasks involve predicting the intensity of emotion, the intensity …...

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 present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic …...

AbstractEfficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc. The issue of data sparsity poses a great …...

AbstractWe present a framework for analyzing what the state in RNNs remembers from its input embeddings. We compute the gradients of the states with respect to the input embeddings and …...

AbstractWe present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing …...

AbstractTraditional sentiment analysis approaches mainly focus on classifying the sentiment polarities or emotion categories of texts. However, they can’t exploit the sentiment intensity information. Therefore, the SemEval-2018 Task 1 is …...

AbstractInterpreting noun compounds is a challenging task. It involves uncovering the underlying predicate which is dropped in the formation of the compound. In most cases, this predicate is of the …...

AbstractHow do neural language models keep track of number agreement between subject and verb? We show that ‘diagnostic classifiers’, trained to predict number from the internal states of a language …...

AbstractThis paper describes a warrant classification system for SemEval 2018 Task 12, that attempts to learn semantic representations of reasons, claims and warrants. The system consists of 3 stacked LSTMs: …...

AbstractVideo captioning has attracted an increasing amount of interest, due in part to its potential for improved accessibility and information retrieval. While existing methods rely on different kinds of visual …...

AbstractIn this work, we develop methods to repurpose text classification neural networks for alternate tasks without modifying the network architecture or parameters. We propose a context based vocabulary remapping method …...

AbstractSemantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model …...

AbstractWith text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of …...

AbstractThis study explores the necessity of performing cross-corpora evaluation for grammatical error correction (GEC) models. GEC models have been previously evaluated based on a single commonly applied corpus: the CoNLL-2014 …...

AbstractThis paper discusses the solution to the problem statement of the SemEval19: EmoContext competition which is ”Contextual Emotion Detection in Texts”. The paper includes the explanation of an architecture that …...

AbstractResearchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words …...

AbstractThe task of Natural Language Inference (NLI) is widely modeled as supervised sentence pair classification. While there has been a lot of work recently on generating explanations of the predictions …...

AbstractThis paper describes the submissions of our team, HAD-Tübingen, for the SemEval 2019 - Task 6: “OffensEval: Identifying and Categorizing Offensive Language in Social Media”. We participated in all the …...

AbstractWe explore the challenge of action prediction from textual descriptions of scenes, a testbed to approximate whether text inference can be used to predict upcoming actions. As a case of …...

AbstractDue to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency. Non-AutoRegressive Translation (NART) …...

AbstractRecent advancements in Internet and Mobile infrastructure have resulted in the development of faster and efficient platforms of communication. These platforms include speech, facial and text-based conversational mediums. Majority of …...

AbstractIn this paper, we describe a suggestion mining system that participated in SemEval 2019 Task 9, SubTask A - Suggestion Mining from Online Reviews and Forums. Given some suggestions from …...

AbstractWe examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone …...

AbstractMulti-relational semantic similarity datasets define the semantic relations between two short texts in multiple ways, e.g., similarity, relatedness, and so on. Yet, all the systems to date designed to capture …...

AbstractWe investigate the extent to which the behavior of neural network language models reflects incremental representations of syntactic state. To do so, we employ experimental methodologies which were originally developed …...

AbstractThe paper presents a system developed for the SemEval-2019 competition Task 5 hat- Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) …...

AbstractThe rise of social media has made information exchange faster and easier among the people. However, in recent times, the use of offensive language has seen an upsurge in social …...

AbstractRecurrent Neural Network Language Models composed of LSTM units, especially those augmented with an external memory, have achieved state-of-the-art results in Language Modeling. However, these models still struggle to process …...

AbstractRecently, there has been a great interest in the development of small and accurate neural networks that run entirely on devices such as mobile phones, smart watches and IoT. This …...

AbstractIn neural network models of language, words are commonly represented using context-invariant representations (word embeddings) which are then put in context in the hidden layers. Since words are often ambiguous, …...

AbstractStacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks …...

AbstractPrior work has shown that, on small amounts of training data, syntactic neural language models learn structurally sensitive generalisations more successfully than sequential language models. However, their computational complexity renders …...

AbstractSelf-attention networks (SAN) have shown promising performance in various Natural Language Processing (NLP) scenarios, especially in machine translation. One of the main points of SANs is the strength of capturing …...

AbstractIn recent years neural language models (LMs) have set the state-of-the-art performance for several benchmarking datasets. While the reasons for their success and their computational demand are well-documented, a comparison …...

AbstractIn this paper we focus on learning dependency aware representations for semantic role labeling without recourse to an external parser. The backbone of our model is an LSTM-based semantic role …...

AbstractPivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation. However, this approach is still challenged by the …...

AbstractThis paper describes the approach of team Kit Kittredge to SemEval-2019 Task 4: Hyperpartisan News Detection. The goal was binary classification of news articles into the categories of “biased” or …...

AbstractRecent work has shown that LSTMs trained on a generic language modeling objective capture syntax-sensitive generalizations such as long-distance number agreement. We have however no mechanistic understanding of how they …...

AbstractIn this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team. For the task, our system takes tweet as an input and determine if …...

AbstractIn this work we present a corpus for the evaluation of sensitive information detection approaches that addresses the need for real world sensitive information for empirical studies. Our sentence corpus …...

Text classification is a popular topic of natural language processing, which has currently at- tracted numerous research efforts worldwide. The significant increase of data in social me- dia requires the …...

AbstractIn this study, we created an automated essay scoring (AES) system for nonnative Japanese learners using an essay dataset with annotations for a holistic score and multiple trait scores, including …...

AbstractWe introduce the first attempt at automatic speech recognition (ASR) in Inuktitut, as a representative for polysynthetic, low-resource languages, like many of the 900 Indigenous languages spoken in the Americas. …...

AbstractWe introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of …...

AbstractA suicide note is usually written shortly before the suicide and it provides a chance to comprehend the self-destructive state of mind of the deceased. From a psychological point of …...

AbstractIn the paper, we present class-based LSTM Russian language models (LMs) with classes generated with the use of both word frequency and linguistic information data, obtained with the help of …...

AbstractEmotion prediction is a critical task in the field of Natural Language Processing (NLP). There has been a significant amount of work done in emotion prediction for resource-rich languages. There …...

AbstractLanguage models (LMs) trained on large quantities of text have been claimed to acquire abstract linguistic representations. Our work tests the robustness of these abstractions by focusing on the ability …...

AbstractSpan identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text. Despite being a staple of …...

AbstractThis paper describes our team work and submission for the SemEval 2020 (Sub-Task A) “Offensive Eval: Identifying and Categorizing Offensive Arabic Language in Arabic Social Media”. Our two baseline models …...

AbstractEmotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional …...

AbstractOnline debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing …...

AbstractThe representations generated by many models of language (word embeddings, recurrent neural networks and transformers) correlate to brain activity recorded while people read. However, these decoding results are usually based …...

AbstractPragmatic inferences often subtly depend on the presence or absence of linguistic features. For example, the presence of a partitive construction (of the) increases the strength of a so-called scalar …...

AbstractIn this paper, we present a multimodal architecture to determine the emotion expressed in a meme. This architecture utilizes both textual and visual information present in a meme. To extract …...

AbstractLSTM-based recurrent neural networks are the state-of-the-art for many natural language processing (NLP) tasks. Despite their performance, it is unclear whether, or how, LSTMs learn structural features of natural languages …...

AbstractWhile neural networks with attention mechanisms have achieved superior performance on many natural language processing tasks, it remains unclear to which extent learned attention resembles human visual attention. In this …...

AbstractLanguages typically provide more than one grammatical construction to express certain types of messages. A speaker’s choice of construction is known to depend on multiple factors, including the choice of …...

AbstractIn this paper, we present our approach and the results of our participation in OffensEval 2020. There are three sub-tasks in OffensEval 2020 namely offensive language identification (sub-task A), automatic …...

AbstractWe propose transfer learning as a method for analyzing the encoding of grammatical structure in neural language models. We train LSTMs on non-linguistic data and evaluate their performance on natural …...

AbstractTo build an interpretable neural text classifier, most of the prior work has focused on designing inherently interpretable models or finding faithful explanations. A new line of work on improving …...

AbstractRecent trends in NLP research have raised an interest in linguistic code-switching (CS); modern approaches have been proposed to solve a wide range of NLP tasks on multiple language pairs. …...

AbstractNeural natural language generation (NLG) models have recently shown remarkable progress in fluency and coherence. However, existing studies on neural NLG are primarily focused on surface-level realizations with limited emphasis …...

AbstractRecent work in NLP shows that LSTM language models capture compositional structure in language data. In contrast to existing work, we consider the learning process that leads to compositional behavior. …...

AbstractChildren have less linguistic skills than adults, which makes it more difficult for them to understand some texts, for instance when browsing the Internet. In this context, we present a …...

AbstractIn this paper, we describe our deep learning system used for SemEval 2020 Task 8: Memotion analysis. We participated in all the subtasks i.e Subtask A: Sentiment classification, Subtask B: …...

AbstractDue to the phenomenal growth of online content in recent time, sentiment analysis has attracted attention of the researchers and developers. A number of benchmark annotated corpora are available for …...

AbstractThis paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to …...

AbstractRecurrent neural networks (RNNs) suffer from well-known limitations and complications which include slow inference and vanishing gradients when processing long sequences in text classification. Recent studies have attempted to accelerate …...

AbstractRecurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input. In most previous experiments, however, learning happens over unrealistic corpora, which do not …...

AbstractThis paper describes SalamNET, an Arabic offensive language detection system that has been submitted to SemEval 2020 shared task 12: Multilingual Offensive Language Identification in Social Media. Our approach focuses …...

AbstractWe introduce a new task of rephrasing for a more natural virtual assistant. Currently, virtual assistants work in the paradigm of intent-slot tagging and the slot values are directly passed …...

AbstractHumans can learn structural properties about a word from minimal experience, and deploy their learned syntactic representations uniformly in different grammatical contexts. We assess the ability of modern neural language …...

AbstractProsody is a rich information source in natural language, serving as a marker for phenomena such as contrast. In order to make this information available to downstream tasks, we need …...

AbstractRecent studies on interpretability of attention distributions have led to notions of faithful and plausible explanations for a model’s predictions. Attention distributions can be considered a faithful explanation if a …...

AbstractIn this work we describe and analyze a supervised learning system for word emphasis selection in phrases drawn from visual media as a part of the Semeval 2020 Shared Task …...

AbstractSemEval Task 4 Commonsense Validation and Explanation Challenge is to validate whether a system can differentiate natural language statements that make sense from those that do not make sense. Two …...

AbstractPrior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement. However, it remains unclear if such an inductive bias would …...

As a language model that integrates traditional symbolic operations and flexible neural repre- sentations, recurrent neural network grammars (RNNGs) have attracted great attention from both scientific and engineering perspectives. However, …...

AbstractRecent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood. In this work, we show that adversarial …...

AbstractAMR (Abstract Meaning Representation) and EDS (Elementary Dependency Structures) are two popular meaning representations in NLP/NLU. AMR is more abstract and conceptual, while EDS is more low level, closer to …...

AbstractPrior studies in multilingual language modeling (e.g., Cotterell et al., 2018; Mielke et al., 2019) disagree on whether or not inflectional morphology makes languages harder to model. We attempt to …...

AbstractOne of the reasons Transformer translation models are popular is that self-attention networks for context modelling can be easily parallelized at sequence level. However, the computational complexity of a self-attention …...

AbstractCode-mixing has become a moving method of communication among multilingual speakers. Most of the social media content of the multilingual societies are written in code-mixed text. However, most of the …...

AbstractText-Based Games (TBGs) have emerged as important testbeds for reinforcement learning (RL) in the natural language domain. Previous methods using LSTM-based action policies are uninterpretable and often overfit the training …...

In intensive care units (ICUs), patient health is monitored through (1) continuous vital signals from various medical devices, and (2) clinical notes consisting of opinions and summaries from doctors which …...

Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from sentences, where each triplet includes an entity, its associated sentiment, and the opinion span explaining the reason for the sentiment. …...

AbstractWe introduce a theoretical framework for understanding and predicting the complexity of sequence classification tasks, using a novel extension of the theory of Boolean function sensitivity. The sensitivity of a …...

AbstractContextual word-representations became a standard in modern natural language processing systems. These models use subword tokenization to handle large vocabularies and unknown words. Word-level usage of such systems requires a …...

AbstractTopic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or …...

AbstractWe introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By framing the task as a sequence …...

AbstractThis paper describes the system developed by the Antwerp Centre for Digital humanities and literary Criticism [UAntwerp] for toxic span detection. We used a stacked generalisation ensemble of five component …...

Visual question answering (VQA) is a hot topic that has recently drew the atten- tion of researchers from domains as di- verse as natural language processing and computer vision. In …...

AbstractFeature attribution a.k.a. input salience methods which assign an importance score to a feature are abundant but may produce surprisingly different results for the same model on the same input. …...

AbstractNeural networks have long been at the center of a debate around the cognitive mechanism by which humans process inflectional morphology. This debate has gravitated into NLP by way of …...

One of the most important factors that sig- nificantly affects the quality of sequence la- beling models is the selection and encod- ing of input features. The complexity and morphological …...

AbstractThis paper describes our two deep learning systems that competed at SemEval-2022 Task 1 “CODWOE: Comparing Dictionaries and WOrd Embeddings”. We participated in the subtask for the reverse dictionary which …...

AbstractWhen a language model is trained to predict natural language sequences, its prediction at each moment depends on a representation of prior context. What kind of information about the prior …...

AbstractThough recent works have focused on modeling high resource languages, the area is still unexplored for low resource languages like Bengali and Hindi. We propose an end to end trainable …...

AbstractWe test the hypothesis that discourse predictability influences Hindi syntactic choice. While prior work has shown that a number of factors (e.g., information status, dependency length, and syntactic surprisal) influence …...

AbstractMost research in the area of automatic essay grading (AEG) is geared towards scoring the essay holistically while there has also been little work done on scoring individual essay traits. …...

AbstractRelation extraction is a key task in Natural Language Processing (NLP), which aims to extract relations between entity pairs from given texts. Recently, relation extraction (RE) has achieved remarkable progress …...

One of the emerging research fields in Natural Language Processing is Noise Detection (ND), the process of identifying posts containing noise information on textual data. While nu- merous datasets and …...

Paraphrase detection allows identifying the degree of likelihood between source and suspect sentences. It is a critical machine learning problem in computational linguistics. This is due to the expression variability …...

AbstractIn several ASR use cases, training and adaptation of domain-specific LMs can only rely on a small amount of manually verified text transcriptions and sometimes a limited amount of in-domain …...

AbstractWe investigate how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Communicative Development Inventory (Fenson et …...

AbstractThere is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and efficient …...

AbstractOnline reviews have become critical in informing purchasing decisions, making the detection of fake reviews a crucial challenge to tackle. Many different Machine Learning based solutions have been proposed, using …...

AbstractDeceptive text poses a significant threat to users, resulting in widespread misinformation and disorder. While researchers have created numerous cutting-edge techniques for detecting deception in domain-specific settings, whether there is …...

AbstractCurrent language models can generate high-quality text. Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions? To tease apart these possibilities, we introduce …...

AbstractWhen acquiring syntax, children consistently choose hierarchical rules over competing non-hierarchical possibilities. Is this preference due to a learning bias for hierarchical structure, or due to more general biases that …...

AbstractIn this paper, we introduce JBLiMP (Japanese Benchmark of Linguistic Minimal Pairs), a novel dataset for targeted syntactic evaluations of language models in Japanese. JBLiMP consists of 331 minimal pairs, …...

AbstractResearch on fake reviews detection and review helpfulness prediction is prevalent, yet most studies tend to focus solely on either fake reviews detection or review helpfulness prediction, considering them separate …...

AbstractDespite the widespread success of Transformers on NLP tasks, recent works have found that they struggle to model several formal languages when compared to recurrent models. This raises the question …...