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S Tadaaki Oshio Japan Patent Information Organization 1....

Speaker-independent system is desirable in many applications where speaker-specific data do not exist. However, if speaker- dependent data are available, the system could be adapted to the specific speaker such …...

Tbe use of neural networks for integrated linguistic analysis may be profitable. This paper presents the first results of our research on that subject: a Hop- field model for syntactical …...

The three corpus-based statistical sense resolution methods studied here attempt to infer the correct sense of a polyse- mous word by using knowledge about patterns of word co- occurrences. The …...

This paper explores use of synergistically-integrated systems of microphone arrays and neural networks for robust speech recognition in variable acoustic environments, where the user must not be encumbered by microphone …...

Text corpora which are tagged with part-of-speech in- formation are useful in many areas of linguistic re- search. In this paper, a new part-of-speech tagging method hased on neural networks …...

The pattern matching capabilities of neural networks can be used to loc- ate syntactic constituents of natural lan- guage. This paper describes a fully auto- mated hybrid system, using neural …...

We describe and experimentally evalu- ate a system, FeasPar, that learns pars- ing spontaneous speech. To train and run FeasPar (Feature Structure Parser), only limited handmodeled knowledge is required. The …...

It is important that the testimony of chil- dren be admissible in court, especially given allegations of abuse. Unfortunately, children can be misled by interrogators or might offer false information, …...

Short Messaging Service (SMS) has become a popular form of communication. While it is predominantly used for monolingual communication, it can be extremely useful for facilitating cross-lingual communication through statistical …...

This paper investigates the use of neural networks for the acquisition of selectional preferences. Inspired by recent advances of neural network models for NLP applica- tions, we propose a neural …...

This paper aims to compare different reg- ularization strategies to address a com- mon phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neu- ral …...

Neural networks have been shown to improve performance across a range of natural-language tasks. However, design- ing and training them can be complicated. Frequently, researchers resort to repeated experimentation to …...

We propose the use of neural networks to model source-side preordering for faster and better statistical machine translation. The neu- ral network trains a logistic regression model to predict whether …...

This paper proposes neural networks for inte- grating compositional and non-compositional sentiment in the process of sentiment compo- sition, a type of semantic composition that op- timizes a sentiment objective. …...

Open domain targeted sentiment is the joint information extraction task that finds target mentions together with the senti- ment towards each mention from a text corpus. The task is typically …...

. This paper proposes a new discriminative framework to train continuous-space trans- lation models based. This framework relies on the definition of a new objective function that al- lows us …...

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 …...

AbstractNowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model …...

AbstractWe assess the reliability and accuracy of (neural) word embeddings for both modern and historical English and German. Our research provides deeper insights into the empirically justified choice of optimal …...

AbstractTransition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art. In this paper, we propose a …...

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 …...

Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which …...

AbstractIn order to organize the large number of products listed in e-commerce sites, each product is usually assigned to one of the multi-level categories in the taxonomy tree. It is …...

AbstractRecently, due to the increasing popularity of social media, the necessity for extracting information from informal text types, such as microblog texts, has gained significant attention. In this study, we …...

Traditional approaches to extractive summarization rely heavily on human- engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a …...

Automatically recognising medical con- cepts mentioned in social media messages (e.g. tweets) enables several applications for enhancing health quality of people in a community, e.g. real-time monitoring of infectious diseases …...

. The article introduces a transition-based constituent parsing method relying on a deep-learning weighting scheme. This weighting method is compared to a structured percep- tron, which is a more traditional …...

In this work, we propose a semi- supervised method for short text clus- tering, where we represent texts as dis- tributed vectors with neural networks, and use a small amount …...

AbstractUsing neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to …...

Parsing accuracy using efficient greedy transi- tion systems has improved dramatically in re- cent years thanks to neural networks. Despite striking results in dependency parsing, how- ever, neural models have …...

We describe our submission to SemEval2016 Task 4: Sentiment Analysis in Twitter. The proposed system ranked first for the sub- task B. Our system comprises of multiple in- dependent models …...

This paper describes our system for aspect- based sentiment analysis (ABSA). We partic- ipate in Subtask 1 (sentence-level ABSA), fo- cusing specifically on aspect category detec- tion. We train a …...

While neural networks have been successfully applied to many NLP tasks the resulting vector- based models are very difficult to interpret. For example it’s not clear how they achieve compositionality, …...

AbstractOur goal is to combine the rich multi-step inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and …...

AbstractTree kernels (TKs) and neural networks are two effective approaches for automatic feature engineering. In this paper, we combine them by modeling context word similarity in semantic TKs. This way, …...

AbstractGender identification in social networks is one of the most popular aspects of user profile learning. Traditionally it has been linked to author profiling, a difficult problem to solve because …...

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 …...

As argumentation about controversies is culture- and language-dependent, porting a serious game that deals with daily argumentation to another language requires substantial adaptation. This article presents a study of deploying …...

AbstractThis paper provides an evaluation of a wide range of advanced sentence-level Quality Estimation models, including Support Vector Regression, Ride Regression, Neural Networks, Gaussian Processes, Bayesian Neural Networks, Deep Kernel …...

AbstractWe investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct …...

AbstractThe paper describes our submissions to task 3 in SemEval-2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask …...

AbstractQuestion answering over knowledge graphs is an important problem of interest both commercially and academically. There is substantial interest in the class of natural language questions that can be answered …...

This paper describes different approaches to improve the transcription and indexing quality of the Fraunhofer IAIS Audio Mining system on Oral History interviews for the Digital Humanities Research. As an …...

Hate speech has become a major issue that is currently a hot topic in the domain of social media. Simultaneously, current proposed methods to address the issue raise concerns about …...

AbstractWe proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in …...

AbstractHow much does “free shipping!” help an advertisement’s ability to persuade? This paper presents two methods for performance attribution: finding the degree to which an outcome can be attributed to …...

AbstractThis paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions, and assign temporal anchors indicating when the possession holds between …...

AbstractThis paper describes the NeuroSent system that participated in SemEval 2018 Task 3. Our system takes a supervised approach that builds on neural networks and word embeddings. Word embeddings were …...

AbstractGiven (i) the rise of a new paradigm to machine translation based on neural networks that results in more fluent and less literal output than previous models and (ii) the …...

AbstractA natural language argument is composed of a claim as well as reasons given as premises for the claim. The warrant explaining the reasoning is usually left implicit, as it …...

AbstractGraphics Processing Units (GPUs) are commonly used to train and evaluate neural networks efficiently. While previous work in deep learning has focused on accelerating operations on dense matrices/tensors on GPUs, …...

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 …...

AbstractWe propose a method based on neural networks to identify the sentiment polarity of opinion words expressed on a specific aspect of a sentence. Although a large majority of works …...

AbstractChinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy. To address the …...

AbstractCompositional generalization is a basic mechanism in human language learning, but current neural networks lack such ability. In this paper, we conduct fundamental research for encoding compositionality in neural networks. …...

AbstractLinguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable …...

AbstractRecent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in …...

AbstractWord Sense Disambiguation (WSD) aims to find the exact sense of an ambiguous word in a particular context. Traditional supervised methods rarely take into consideration the lexical resources like WordNet, …...

AbstractRecently, neural networks have shown promising results on Document-level Aspect Sentiment Classification (DASC). However, these approaches often offer little transparency w.r.t. their inner working mechanisms and lack interpretability. In this …...

AbstractWe consider a document classification problem where document labels are absent but only relevant keywords of a target class and unlabeled documents are given. Although heuristic methods based on pseudo-labeling …...

AbstractRecent work has shown that current text classification models are fragile and sensitive to simple perturbations. In this work, we propose a novel adversarial training approach, LexicalAT, to improve the …...

. This survey takes place in the general context of information extraction and presents more particularly the successive approaches to supervised event extraction from texts. The first rule-based systems and …...

AbstractNeural Networks trained with gradient descent are known to be susceptible to catastrophic forgetting caused by parameter shift during the training process. In the context of Neural Machine Translation (NMT) …...

AbstractMeaning conflation deficiency is one of the main limiting factors of word representations which, given their widespread use at the core of many NLP systems, can lead to inaccurate semantic …...

AbstractUnsupervised domain adaptation (UDA) is the task of training a statistical model on labeled data from a source domain to achieve better performance on data from a target domain, with …...

Neural networks have become the state- of-the-art approach for machine transla- tion (MT) in many languages. While linguistically-motivated tokenization tech- niques were shown to have significant ef- fects on the …...

. This survey takes place in the general context of information extraction and presents more particularly the successive approaches to supervised event extraction from texts. The first rule-based systems and …...

AbstractKeyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated …...

AbstractAligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts …...

AbstractNowadays, social media credibility is a pressing issue for each of us who are living in an altered online landscape. The speed of news diffusion is striking. Given the popularity …...

AbstractThis paper describes a method and system to solve the problem of detecting offensive language in social media using anti-adversarial features. Our submission to the SemEval-2020 task 12 challenge was …...

AbstractThis paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that …...

AbstractIn the last few years, a number of successful approaches have emerged that are able to adequately model various aspects of natural language. In particular, language models based on neural …...

AbstractNeural networks can achieve impressive performance on many natural language processing applications, but they typically need large labeled data for training and are not easily interpretable. On the other hand, …...

AbstractRecent progress in neural machine translation (NMT) is directed towards larger neural networks trained on an increasing amount of hardware resources. As a result, NMT models are costly to train, …...

AbstractWhile state-of-the-art models that rely upon massively multilingual pretrained encoders achieve sample efficiency in downstream applications, they still require abundant amounts of unlabelled text. Nevertheless, most of the world’s languages …...

AbstractWe explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natural logic formalism while introducing …...

AbstractGenerating explanations for neural networks has become crucial for their applications in real-world with respect to reliability and trustworthiness. In natural language processing, existing methods usually provide important features which …...

AbstractThe success of neural networks on a diverse set of NLP tasks has led researchers to question how much these networks actually “know” about natural language. Probes are a natural …...

AbstractKnowledge bases are increasingly exploited as gold standard data sources which benefit various knowledge-driven NLP tasks. In this paper, we explore a new research direction to perform knowledge base (KB) …...

AbstractWe propose a mixed methods approach to the identification of scribes and authors in handwritten documents, and present LiViTo, a software tool which combines linguistic insights and computer vision techniques …...

AbstractNeural networks are surprisingly good at interpolating and perform remarkably well when the training set examples resemble those in the test set. However, they are often unable to extrapolate patterns …...

AbstractIn this paper, we present a study on a French Spoken Language Understanding (SLU) task: the MEDIA task. Many works and studies have been proposed for many tasks, but most …...

AbstractMost of the research on text simplification is limited to sentence level nowadays. In this paper, we are the first to investigate the helpfulness of document context on sentence simplification …...

AbstractThe focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment …...

Detecting metaphors is challenging due to the subtle ontological differences between metaphorical and non-metaphorical expres- sions. Neural networks have been widely adopted in metaphor detection and become the main stream …...

AbstractIn this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development …...

AbstractPredicting reading time has been a subject of much previous work, focusing on how different words affect human processing, measured by reading time. However, previous work has dealt with a …...

With the rapid advancement of machine learning and deep learning, a great breakthrough has been achieved in many areas of natural language processing in recent years. Complex language tasks, such …...

AbstractModern classification models tend to struggle when the amount of annotated data is scarce. To overcome this issue, several neural few-shot classification models have emerged, yielding significant progress over time, …...

Data augmentation has recently seen increased interest in NLP due to more work in low- resource domains, new tasks, and the popu- larity of large-scale neural networks that re- quire …...

In this paper, we propose a novel method which establishes a newborn relation between Signal Processing and Natural Language Pro- cessing (NLP) method via Variational Mode Decomposition (VMD). Unlike the …...

AbstractThere is a growing consensus that surface form alone does not enable models to learn meaning and gain language understanding. This warrants an interest in hybrid systems that combine the …...

Recently, quite a few studies have been progressive for temporal relation extraction, which is an important work used in sev- eral natural language processing applications. However, less concentration has been …...

AbstractNeural networks (NN) applied to natural language processing (NLP) are becoming deeper and more complex, making them increasingly difficult to understand and interpret. Even in applications of limited scope on …...

AbstractRecent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales. Most existing methods distribute their …...

AbstractRecently, there has been significant progress in studying neural networks for translating text descriptions into SQL queries under the zero-shot cross-domain setting. Despite achieving good performance on some public benchmarks, …...

AbstractIn this paper, we introduce Integrated Directional Gradients (IDG), a method for attributing importance scores to groups of features, indicating their relevance to the output of a neural network model …...

AbstractDetermining whether two documents were composed by the same author, also known as authorship verification, has traditionally been tackled using statistical methods. Recently, authorship representations learned using neural networks have …...

AbstractRecent studies on neural networks with pre-trained weights (i.e., BERT) have mainly focused on a low-dimensional subspace, where the embedding vectors computed from input words (or their contexts) are located. …...

AbstractWhile neural networks produce state-of-the- art performance in several NLP tasks, they generally depend heavily on lexicalized information, which transfer poorly between domains. Previous works have proposed delexicalization as a …...

AbstractRecently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries. Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely …...

AbstractIn this paper, we explore the task of automatically generating natural language descriptions of salient patterns in a time series, such as stock prices of a company over a week. …...

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 …...

AbstractAutomatic math problem solving has attracted much attention of NLP researchers recently. However, most of the works focus on the solving of Math Word Problems (MWPs). In this paper, we …...

AbstractTo date, there has been no resource for studying discourse coherence on real-world Danish texts. Discourse coherence has mostly been approached with the assumption that incoherent texts can be represented …...

AbstractTo maximize the accuracy and increase the overall acceptance of text classifiers, we propose a framework for the efficient, in-operation moderation of classifiers’ output. Our framework focuses on use cases …...

Various methods have been proposed to generate code-switched texts. Many of these involve training neural networks and, in turn, require some (albeit small) amounts of code-switched texts or parallel corpora …...

AbstractHate speech is plaguing the cyberspace along with user-generated content. Adding counter speech has become an effective way to combat hate speech online. Existing datasets and models target either (a) …...

AbstractThe performance of deep learning models in NLP and other fields of machine learning has led to a rise in their popularity, and so the need for explanations of these …...

AbstractNeural networks are widely used in various NLP tasks for their remarkable performance. However, the complexity makes them difficult to interpret, i.e., they are not guaranteed right for the right …...

AbstractThe existing research efforts in Multimodal Sentiment Analysis (MSA) have focused on developing the expressive ability of neural networks to fuse information from different modalities. However, these approaches lack a …...

AbstractWord ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language models have not …...

AbstractIn this article, we present Quevedo, a software tool we have developed for the task of automatic processing of graphical languages. These are languages which use images to convey meaning, …...

AbstractIn this manuscript we detail the participation of the UMUTeam in the iSarcasm shared task (SemEval-2022). This shared task is related to the identification of sarcasm in English and Arabic …...

Automatic generation of questions about the given context is useful for the adaptation of question-answering systems or to support edu- cation. We trained and evaluated a model that generates a …...