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Many parsers learn sparse class distributions over trees to model natural language. Recursive Neural Networks (RNN) use much denser representations, yet can still achieve an F-score of 92.06% for right …...

We present a joint language and transla- tion model based on a recurrent neural net- work which predicts target words based on an unbounded history of both source and tar- …...

We introduce recurrent neural network- based Minimum Translation Unit (MTU) models which make predictions based on an unbounded history of previous bilin- gual contexts. Traditional back-off n-gram models suffer under …...

In this paper, we propose a recurrent neural network-based tuple sequence model (RNNTSM) that can help phrase-based translation model overcome the phrasal independence assumption. Our RNNTSM can potentially capture arbitrary …...

This study proposes a word alignment model based on a recurrent neural net- work (RNN), in which an unlimited alignment history is represented by re- currently connected hidden layers. We …...

We propose a conversion of bilingual sentence pairs and the corresponding word alignments into novel linear se- quences. These are joint translation and reordering (JTR) uniquely defined sequences, combining interdepending …...

Arabic, Hebrew, and similar languages are typi- cally written without diacritics, leading to ambigu- ity and posing a major challenge for core language processing tasks like speech recognition. Previous approaches …...

Document level sentiment classification remains a challenge: encoding the intrin- sic relations between sentences in the se- mantic meaning of a document. To ad- dress this, we introduce a neural …...

We propose a discriminatively trained re- current neural network (RNN) that pre- dicts the actions for a fast and accurate shift-reduce dependency parser. The RNN uses its output-dependent model struc- …...

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set. Due to the great challenge of constructing the …...

This paper describes the statistical machine translation system developed at RWTH Aachen University for the German→English translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). …...

Modeling interactions between two sen- tences is crucial for a number of natu- ral language processing tasks including Answer Selection, Dialogue Act Analy- sis, etc. While deep learning methods like …...

To create conversational systems working in actual situations, it is crucial to assume that they interact with multiple agents. In this work, we tackle addressee and response se- lection for …...

We submitted two systems to the SemEval- 2016 Task 12: Clinical TempEval challenge, participating in Phase 1, where we identified text spans of time and event expressions in clinical notes …...

Sentiment lexicons have been leveraged as a useful source of features for sentiment anal- ysis models, leading to the state-of-the-art accuracies. On the other hand, most ex- isting methods use …...

AbstractNeural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a …...

AbstractAutomatic video description generation has recently been getting attention after rapid advancement in image caption generation. Automatically generating description for a video is more challenging than for an image due …...

Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complemen- tary approaches. Distributional methods, whose supervised variants are the cur- rent best …...

In this paper, with the help of knowl- edge base, we build and formulate a se- mantic space to connect the source and target languages, and apply it to the …...

AbstractWe propose two models for verbalizing numbers, a key component in speech recognition and synthesis systems. The first model uses an end-to-end recurrent neural network. The second model, drawing inspiration …...

Moving from limited-domain natural lan- guage generation (NLG) to open domain is difficult because the number of semantic in- put combinations grows exponentially with the number of domains. Therefore, it …...

AbstractRecently Wen et al. (2015) have proposed a Recurrent Neural Network (RNN) approach to the generation of utterances from dialog acts, and shown that although their model requires less effort …...

AbstractAn interactive Question Answering (QA) system frequently encounters non-sentential (incomplete) questions. These non-sentential questions may not make sense to the system when a user asks them without the context of …...

Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical er- ror correction. Current approaches gener- ally focus on relatively shallow semantics …...

AbstractThis paper presents a novel approach to recurrent neural network (RNN) regularization. Differently from the widely adopted dropout method, which is applied to forward connections of feedforward architectures or RNNs, …...

We propose recurrent support vector machine (RSVM) for slot tagging. This model is a combi- nation of the recurrent neural network (RNN) and the structured support vector machine. RNN extracts …...

We propose an approach for semantic parsing that uses a recurrent neural net- work to map a natural language question into a logical form representation of a KB query. Building …...

AbstractWe present Sparse Non-negative Matrix (SNM) estimation, a novel probability estimation technique for language modeling that can efficiently incorporate arbitrary features. We evaluate SNM language models on two corpora: the …...

AbstractRecurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN …...

In this study, we introduce a non- deterministic method for referring expres- sion generation. We describe two models that account for individual variation in the choice of referential form in …...

AbstractWe study the representation and encoding of phonemes in a recurrent neural network model of grounded speech. We use a model which processes images and their spoken descriptions, and projects …...

Social media is an important source for news writers. However, extracting useful informa- tion for news writers from the vast amount of social media information is laborious. There- fore, services …...

AbstractIn this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and …...

AbstractWe investigate the generation of one-sentence Wikipedia biographies from facts derived from Wikidata slot-value pairs. We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate …...

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 …...

AbstractFirst-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many …...

AbstractRecognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are context-dependent and include long, varied and unconventional descriptions as compared to more formal medical symptom …...

AbstractWe propose a novel framework based on neural networks to identify the sentiment of opinion targets in a comment/review. Our framework adopts multiple-attention mechanism to capture sentiment features separated by …...

AbstractIn this paper we introduce a practical first step towards the creation of an automated debate agent: a state-of-the-art recurrent predictive model for predicting debate winners. By having an accurate …...

AbstractHumans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same. In this paper, …...

This paper presents a benchmark dataset for the task of inter-sentence relation extraction. The paper explains the distant supervision method followed for creating the dataset for inter-sentence relation extraction, involving …...

This paper presents a recurrent neural network model to automate the analysis of students’ computational thinking in problem-solving dialogue. We have collected and annotated dialogue transcripts from middle school students …...

AbstractWe present a video captioning approach that encodes features by progressively completing syntactic structure (LSTM-CSS). To construct basic syntactic structure (i.e., subject, predicate, and object), we use a Conditional Random …...

AbstractGenerating character-level features is an important step for achieving good results in various natural language processing tasks. To alleviate the need for human labor in generating hand-crafted features, methods that …...

AbstractA great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. As the studies …...

AbstractIdentifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield …...

AbstractWe take a multi-task learning approach to the shared Task 1 at SemEval-2018. The general idea concerning the model structure is to use as little external data as possible in …...

AbstractMost word representation learning methods are based on the distributional hypothesis in linguistics, according to which words that are used and occur in the same contexts tend to possess similar …...

This study presents a long short-term memory (LSTM) neural network approach to Japanese word segmentation (JWS). Previous studies on Chinese word segmentation have succeeded in using recurrent neural networks such …...

AbstractThis paper describes MuTuX, our system that is designed for task 1-5a, emotion classification analysis of tweets on SemEval2018. The system aims at exploring the potential of context information of …...

AbstractWe propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using …...

AbstractAlthough neural machine translation with the encoder-decoder framework has achieved great success recently, it still suffers drawbacks of forgetting distant information, which is an inherent disadvantage of recurrent neural network …...

AbstractThis paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study …...

AbstractWritten communication lacks the multimodal features such as posture, gesture and gaze that make it easy to model affective states. Especially in social media such as Twitter, due to the …...

AbstractNeural network approaches to Named-Entity Recognition reduce the need for carefully hand-crafted features. While some features do remain in state-of-the-art systems, lexical features have been mostly discarded, with the exception …...

AbstractIn this work, we customized a neural machine translation system for translation of subtitles in the domain of entertainment. The neural translation model was adapted to the subtitling content and …...

AbstractWe propose a novel deep structured learning framework for event temporal relation extraction. The model consists of 1) a recurrent neural network (RNN) to learn scoring functions for pair-wise relations, …...

AbstractSocial media has an increasing amount of information that both customers and companies can benefit from. These social media posts can include Tweets or be in the form of vocalization …...

AbstractGenerating a long, coherent text such as a paragraph requires a high-level control of different levels of relations between sentences (e.g., tense, coreference). We call such a logical connection between …...

AbstractWe introduce neural finite state transducers (NFSTs), a family of string transduction models defining joint and conditional probability distributions over pairs of strings. The probability of a string pair is …...

This paper is devoted to developing a re- current neural network (RNN) solution for segmenting the unpunctuated transcripts generated by automatic speech recognition for simultaneous interpretation. RNNs are effective in …...

AbstractUsers participate in online discussion forums to learn from others and share their knowledge with the community. They often start a thread with a question or by sharing their new …...

AbstractDifferent from other text generation tasks, in product description generation, it is of vital importance to generate faithful descriptions that stick to the product attribute information. However, little attention has …...

AbstractMorphological segmentation of words is the process of dividing a word into smaller units called morphemes; it is tricky especially when a morphologically rich or polysynthetic language is under question. …...

AbstractTo ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segment-level phonotactic acquisition, we compare the performance of two recurrent neural network …...

AbstractChinese is a logographic writing system, and the shape of Chinese characters contain rich syntactic and semantic information. In this paper, we propose a model to learn Chinese word embeddings …...

AbstractHow language-agnostic are current state-of-the-art NLP tools? Are there some types of language that are easier to model with current methods? In prior work (Cotterell et al., 2018) we attempted …...

AbstractThis paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing. Our method describes a decomposition of the overall labeling task into subtasks and …...

AbstractThis paper illustrates our approach to the shared task on similar language translation in the fifth conference on machine translation (WMT-20). Our motivation comes from the latest state of the …...

AbstractAutomatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology …...

AbstractIt is well known that deep learning model has huge parameters and is computationally expensive, especially for embedded and mobile devices. Polyphone pronunciations selection is a basic function for Chinese …...

AbstractWe present a neural network approach to estimate the communication style of spoken interaction, namely the stylistic variations elaborateness and directness, and investigate which type of input features to the …...

AbstractThis paper describes our contribution to SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. Here we present a method based on a deep neural network. In recent years, quite …...

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 …...

AbstractPredicting users’ opinions in their response to social events has important real-world applications, many of which political and social impacts. Existing approaches derive a population’s opinion on a going event …...

AbstractOffensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that is addressed towards an individual or a group. Due to …...

AbstractThere has been significant progress in recent years in the field of Natural Language Processing thanks to the introduction of the Transformer architecture. Current state-of-the-art models, via a large number …...

AbstractThis paper presents a new challenging information extraction task in the domain of materials science. We develop an annotation scheme for marking information on experiments related to solid oxide fuel …...

AbstractEstimating uncertainties of Neural Network predictions paves the way towards more reliable and trustful text classifications. However, common uncertainty estimation approaches remain as black-boxes without explaining which features have led …...

AbstractThe transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. Yet for character-level transduction tasks, e.g. morphological inflection generation and historical text normalization, …...

AbstractThe current natural language processing is strongly focused on raising accuracy. The progress comes at a cost of super-heavy models with hundreds of millions or even billions of parameters. However, …...

The attention layer has become a prevalent component in improving the effectiveness of neural network models for NLP tasks. Figur- ing out why attention is effective and its inter- pretability …...

AbstractSequential information, a.k.a., orders, is assumed to be essential for processing a sequence with recurrent neural network or convolutional neural network based encoders. However, is it possible to encode natural …...

AbstractOptical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic variations, typefaces, or language evolution (i.e., new letters, word spellings), as …...

AbstractSource code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality. An …...

AbstractOpinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. …...

AbstractThis paper examines the effectiveness of different feature representations of audio data in accurately classifying discourse meaning in Spanish. The task involves determining whether an utterance is a declarative sentence, …...