Kentaro Inui


2020

pdf bib
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese
Tatsuki Kuribayashi | Takumi Ito | Jun Suzuki | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LM-based method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) parallels between LMs and human word order preference, and (ii) consistency of the results obtained using the LM-based method with previous linguistic studies. Through our experiments, we tentatively conclude that LMs display sufficient word order knowledge for usage as an analysis tool. Finally, using the LM-based method, we demonstrate the relationship between the canonical word order and topicalization, which had yet to be analyzed by large-scale experiments.

pdf bib
Evaluating Dialogue Generation Systems via Response Selection
Shiki Sato | Reina Akama | Hiroki Ouchi | Jun Suzuki | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Existing automatic evaluation metrics for open-domain dialogue response generation systems correlate poorly with human evaluation. We focus on evaluating response generation systems via response selection. To evaluate systems properly via response selection, we propose a method to construct response selection test sets with well-chosen false candidates. Specifically, we propose to construct test sets filtering out some types of false candidates: (i) those unrelated to the ground-truth response and (ii) those acceptable as appropriate responses. Through experiments, we demonstrate that evaluating systems via response selection with the test set developed by our method correlates more strongly with human evaluation, compared with widely used automatic evaluation metrics such as BLEU.

pdf bib
Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
Masahiro Kaneko | Masato Mita | Shun Kiyono | Jun Suzuki | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correction (GEC). The answer to this question is not as straightforward as one might expect because the previous common methods for incorporating a MLM into an EncDec model have potential drawbacks when applied to GEC. For example, the distribution of the inputs to a GEC model can be considerably different (erroneous, clumsy, etc.) from that of the corpora used for pre-training MLMs; however, this issue is not addressed in the previous methods. Our experiments show that our proposed method, where we first fine-tune a MLM with a given GEC corpus and then use the output of the fine-tuned MLM as additional features in the GEC model, maximizes the benefit of the MLM. The best-performing model achieves state-of-the-art performances on the BEA-2019 and CoNLL-2014 benchmarks. Our code is publicly available at: https://github.com/kanekomasahiro/bert-gec.

pdf bib
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?
Hitomi Yanaka | Koji Mineshima | Daisuke Bekki | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. In this paper, we introduce a method for evaluating whether neural models can learn systematicity of monotonicity inference in natural language, namely, the regularity for performing arbitrary inferences with generalization on composition. We consider four aspects of monotonicity inferences and test whether the models can systematically interpret lexical and logical phenomena on different training/test splits. A series of experiments show that three neural models systematically draw inferences on unseen combinations of lexical and logical phenomena when the syntactic structures of the sentences are similar between the training and test sets. However, the performance of the models significantly decreases when the structures are slightly changed in the test set while retaining all vocabularies and constituents already appearing in the training set. This indicates that the generalization ability of neural models is limited to cases where the syntactic structures are nearly the same as those in the training set.

pdf bib
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition
Hiroki Ouchi | Jun Suzuki | Sosuke Kobayashi | Sho Yokoi | Tatsuki Kuribayashi | Ryuto Konno | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Interpretable rationales for model predictions play a critical role in practical applications. In this study, we develop models possessing interpretable inference process for structured prediction. Specifically, we present a method of instance-based learning that learns similarities between spans. At inference time, each span is assigned a class label based on its similar spans in the training set, where it is easy to understand how much each training instance contributes to the predictions. Through empirical analysis on named entity recognition, we demonstrate that our method enables to build models that have high interpretability without sacrificing performance.

pdf bib
R4C: A Benchmark for Evaluating RC Systems to Get the Right Answer for the Right Reason
Naoya Inoue | Pontus Stenetorp | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent studies have revealed that reading comprehension (RC) systems learn to exploit annotation artifacts and other biases in current datasets. This prevents the community from reliably measuring the progress of RC systems. To address this issue, we introduce R4C, a new task for evaluating RC systems’ internal reasoning. R4C requires giving not only answers but also derivations: explanations that justify predicted answers. We present a reliable, crowdsourced framework for scalably annotating RC datasets with derivations. We create and publicly release the R4C dataset, the first, quality-assured dataset consisting of 4.6k questions, each of which is annotated with 3 reference derivations (i.e. 13.8k derivations). Experiments show that our automatic evaluation metrics using multiple reference derivations are reliable, and that R4C assesses different skills from an existing benchmark.

pdf bib
Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
Takuma Kato | Kaori Abe | Hiroki Ouchi | Shumpei Miyawaki | Jun Suzuki | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

In general, the labels used in sequence labeling consist of different types of elements. For example, IOB-format entity labels, such as B-Person and I-Person, can be decomposed into span (B and I) and type information (Person). However, while most sequence labeling models do not consider such label components, the shared components across labels, such as Person, can be beneficial for label prediction. In this work, we propose to integrate label component information as embeddings into models. Through experiments on English and Japanese fine-grained named entity recognition, we demonstrate that the proposed method improves performance, especially for instances with low-frequency labels.

pdf bib
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation
Hiroaki Funayama | Shota Sasaki | Yuichiroh Matsubayashi | Tomoya Mizumoto | Jun Suzuki | Masato Mita | Kentaro Inui
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Many recent Short Answer Scoring (SAS) systems have employed Quadratic Weighted Kappa (QWK) as the evaluation measure of their systems. However, we hypothesize that QWK is unsatisfactory for the evaluation of the SAS systems when we consider measuring their effectiveness in actual usage. We introduce a new task formulation of SAS that matches the actual usage. In our formulation, the SAS systems should extract as many scoring predictions that are not critical scoring errors (CSEs). We conduct the experiments in our new task formulation and demonstrate that a typical SAS system can predict scores with zero CSE for approximately 50% of test data at maximum by filtering out low-reliablility predictions on the basis of a certain confidence estimation. This result directly indicates the possibility of reducing half the scoring cost of human raters, which is more preferable for the evaluation of SAS systems.

pdf bib
Creating Corpora for Research in Feedback Comment Generation
Ryo Nagata | Kentaro Inui | Shin’ichiro Ishikawa
Proceedings of the 12th Language Resources and Evaluation Conference

In this paper, we report on datasets that we created for research in feedback comment generation — a task of automatically generating feedback comments such as a hint or an explanatory note for writing learning. There has been almost no such corpus open to the public and accordingly there has been a very limited amount of work on this task. In this paper, we first discuss the principle and guidelines for feedback comment annotation. Then, we describe two corpora that we have manually annotated with feedback comments (approximately 50,000 general comments and 6,700 on preposition use). A part of the annotation results is now available on the web, which will facilitate research in feedback comment generation

pdf bib
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension
Diana Galvan-Sosa | Jun Suzuki | Kyosuke Nishida | Koji Matsuda | Kentaro Inui
Proceedings of the Second Workshop on Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems. As the name suggests, common sense is related to perception and as such, humans derive it from experience rather than from literary education. Recent works in the NLP and the computer vision field have made the effort of making such knowledge explicit using written language and visual inputs, respectively. Our premise is that the latter source fits better with the characteristics of commonsense acquisition. In this work, we explore to what extent the descriptions of real-world scenes are sufficient to learn common sense about different daily situations, drawing upon visual information to answer script knowledge questions.

pdf bib
A Self-Refinement Strategy for Noise Reduction in Grammatical Error Correction
Masato Mita | Shun Kiyono | Masahiro Kaneko | Jun Suzuki | Kentaro Inui
Findings of the Association for Computational Linguistics: EMNLP 2020

Existing approaches for grammatical error correction (GEC) largely rely on supervised learning with manually created GEC datasets. However, there has been little focus on verifying and ensuring the quality of the datasets, and on how lower-quality data might affect GEC performance. We indeed found that there is a non-negligible amount of “noise” where errors were inappropriately edited or left uncorrected. To address this, we designed a self-refinement method where the key idea is to denoise these datasets by leveraging the prediction consistency of existing models, and outperformed strong denoising baseline methods. We further applied task-specific techniques and achieved state-of-the-art performance on the CoNLL-2014, JFLEG, and BEA-2019 benchmarks. We then analyzed the effect of the proposed denoising method, and found that our approach leads to improved coverage of corrections and facilitated fluency edits which are reflected in higher recall and overall performance.

pdf bib
Efficient Estimation of Influence of a Training Instance
Sosuke Kobayashi | Sho Yokoi | Jun Suzuki | Kentaro Inui
Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing

Understanding the influence of a training instance on a neural network model leads to improving interpretability. However, it is difficult and inefficient to evaluate the influence, which shows how a model’s prediction would be changed if a training instance were not used. In this paper, we propose an efficient method for estimating the influence. Our method is inspired by dropout, which zero-masks a sub-network and prevents the sub-network from learning each training instance. By switching between dropout masks, we can use sub-networks that learned or did not learn each training instance and estimate its influence. Through experiments with BERT and VGGNet on classification datasets, we demonstrate that the proposed method can capture training influences, enhance the interpretability of error predictions, and cleanse the training dataset for improving generalization.

pdf bib
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions
Takaki Otake | Sho Yokoi | Naoya Inoue | Ryo Takahashi | Tatsuki Kuribayashi | Kentaro Inui
Proceedings of the 28th International Conference on Computational Linguistics

Events in a narrative differ in salience: some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool for text analysis in narratology and folkloristics. To compute event salience without any annotations, we adopt Barthes’ definition of event salience and propose several unsupervised methods that require only a pre-trained language model. Evaluating the proposed methods on folktales with event salience annotation, we show that the proposed methods outperform baseline methods and find fine-tuning a language model on narrative texts is a key factor in improving the proposed methods.

pdf bib
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution
Ryuto Konno | Yuichiroh Matsubayashi | Shun Kiyono | Hiroki Ouchi | Ryo Takahashi | Kentaro Inui
Proceedings of the 28th International Conference on Computational Linguistics

One critical issue of zero anaphora resolution (ZAR) is the scarcity of labeled data. This study explores how effectively this problem can be alleviated by data augmentation. We adopt a state-of-the-art data augmentation method, called the contextual data augmentation (CDA), that generates labeled training instances using a pretrained language model. The CDA has been reported to work well for several other natural language processing tasks, including text classification and machine translation. This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data. We also propose two methods to adapt CDA to ZAR: [MASK]-based augmentation and linguistically-controlled masking. Consequently, the experimental results on Japanese ZAR show that our methods contribute to both the accuracy gainand the computation cost reduction. Our closer analysis reveals that the proposed method can improve the quality of the augmented training data when compared to the conventional CDA.

pdf bib
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
Ryo Fujii | Masato Mita | Kaori Abe | Kazuaki Hanawa | Makoto Morishita | Jun Suzuki | Kentaro Inui
Proceedings of the 28th International Conference on Computational Linguistics

Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain. However, existing studies suggest that NMT still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. To make better use of NMT for cross-cultural communication, one of the most promising directions is to develop a model that correctly handles these expressions. Though its importance has been recognized, it is still not clear as to what creates the great gap in performance between the translation of clean input and that of UGC. To answer the question, we present a new dataset, PheMT, for evaluating the robustness of MT systems against specific linguistic phenomena in Japanese-English translation. Our experiments with the created dataset revealed that not only our in-house models but even widely used off-the-shelf systems are greatly disturbed by the presence of certain phenomena.

pdf bib
A System for Worldwide COVID-19 Information Aggregation
Akiko Aizawa | Frederic Bergeron | Junjie Chen | Fei Cheng | Katsuhiko Hayashi | Kentaro Inui | Hiroyoshi Ito | Daisuke Kawahara | Masaru Kitsuregawa | Hirokazu Kiyomaru | Masaki Kobayashi | Takashi Kodama | Sadao Kurohashi | Qianying Liu | Masaki Matsubara | Yusuke Miyao | Atsuyuki Morishima | Yugo Murawaki | Kazumasa Omura | Haiyue Song | Eiichiro Sumita | Shinji Suzuki | Ribeka Tanaka | Yu Tanaka | Masashi Toyoda | Nobuhiro Ueda | Honai Ueoka | Masao Utiyama | Ying Zhong
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.

pdf bib
Filtering Noisy Dialogue Corpora by Connectivity and Content Relatedness
Reina Akama | Sho Yokoi | Jun Suzuki | Kentaro Inui
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Large-scale dialogue datasets have recently become available for training neural dialogue agents. However, these datasets have been reported to contain a non-negligible number of unacceptable utterance pairs. In this paper, we propose a method for scoring the quality of utterance pairs in terms of their connectivity and relatedness. The proposed scoring method is designed based on findings widely shared in the dialogue and linguistics research communities. We demonstrate that it has a relatively good correlation with the human judgment of dialogue quality. Furthermore, the method is applied to filter out potentially unacceptable utterance pairs from a large-scale noisy dialogue corpus to ensure its quality. We experimentally confirm that training data filtered by the proposed method improves the quality of neural dialogue agents in response generation.

pdf bib
Word Rotator’s Distance
Sho Yokoi | Ryo Takahashi | Reina Akama | Jun Suzuki | Kentaro Inui
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

One key principle for assessing textual similarity is measuring the degree of semantic overlap between texts by considering the word alignment. Such alignment-based approaches are both intuitive and interpretable; however, they are empirically inferior to the simple cosine similarity between general-purpose sentence vectors. We focus on the fact that the norm of word vectors is a good proxy for word importance, and the angle of them is a good proxy for word similarity. However, alignment-based approaches do not distinguish the norm and direction, whereas sentence-vector approaches automatically use the norm as the word importance. Accordingly, we propose decoupling word vectors into their norm and direction then computing the alignment-based similarity with the help of earth mover’s distance (optimal transport), which we refer to as word rotator’s distance. Furthermore, we demonstrate how to grow the norm and direction of word vectors (vector converter); this is a new systematic approach derived from the sentence-vector estimation methods, which can significantly improve the performance of the proposed method. On several STS benchmarks, the proposed methods outperform not only alignment-based approaches but also strong baselines. The source code is avaliable at https://github.com/eumesy/wrd

pdf bib
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms
Goro Kobayashi | Tatsuki Kuribayashi | Sho Yokoi | Kentaro Inui
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Attention is a key component of Transformers, which have recently achieved considerable success in natural language processing. Hence, attention is being extensively studied to investigate various linguistic capabilities of Transformers, focusing on analyzing the parallels between attention weights and specific linguistic phenomena. This paper shows that attention weights alone are only one of the two factors that determine the output of attention and proposes a norm-based analysis that incorporates the second factor, the norm of the transformed input vectors. The findings of our norm-based analyses of BERT and a Transformer-based neural machine translation system include the following: (i) contrary to previous studies, BERT pays poor attention to special tokens, and (ii) reasonable word alignment can be extracted from attention mechanisms of Transformer. These findings provide insights into the inner workings of Transformers.

pdf bib
Langsmith: An Interactive Academic Text Revision System
Takumi Ito | Tatsuki Kuribayashi | Masatoshi Hidaka | Jun Suzuki | Kentaro Inui
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Despite the current diversity and inclusion initiatives in the academic community, researchers with a non-native command of English still face significant obstacles when writing papers in English. This paper presents the Langsmith editor, which assists inexperienced, non-native researchers to write English papers, especially in the natural language processing (NLP) field. Our system can suggest fluent, academic-style sentences to writers based on their rough, incomplete phrases or sentences. The system also encourages interaction between human writers and the computerized revision system. The experimental results demonstrated that Langsmith helps non-native English-speaker students write papers in English. The system is available at https://emnlp-demo.editor. langsmith.co.jp/.

2019

pdf bib
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Kentaro Inui | Jing Jiang | Vincent Ng | Xiaojun Wan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

pdf bib
Select and Attend: Towards Controllable Content Selection in Text Generation
Xiaoyu Shen | Jun Suzuki | Kentaro Inui | Hui Su | Dietrich Klakow | Satoshi Sekine
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling content selection from the decoder. The decoupled content selection is human interpretable, whose value can be manually manipulated to control the content of generated text. The model can be trained end-to-end without human annotations by maximizing a lower bound of the marginal likelihood. We further propose an effective way to trade-off between performance and controllability with a single adjustable hyperparameter. In both data-to-text and headline generation tasks, our model achieves promising results, paving the way for controllable content selection in text generation.

pdf bib
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction
Shun Kiyono | Jun Suzuki | Masato Mita | Tomoya Mizumoto | Kentaro Inui
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models. However, consensus is lacking on experimental configurations, namely, choosing how the pseudo data should be generated or used. In this study, these choices are investigated through extensive experiments, and state-of-the-art performance is achieved on the CoNLL-2014 test set (F0.5=65.0) and the official test set of the BEA-2019 shared task (F0.5=70.2) without making any modifications to the model architecture.

pdf bib
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis
Hiroki Ouchi | Jun Suzuki | Kentaro Inui
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

In transductive learning, an unlabeled test set is used for model training. Although this setting deviates from the common assumption of a completely unseen test set, it is applicable in many real-world scenarios, wherein the texts to be processed are known in advance. However, despite its practical advantages, transductive learning is underexplored in natural language processing. Here we conduct an empirical study of transductive learning for neural models and demonstrate its utility in syntactic and semantic tasks. Specifically, we fine-tune language models (LMs) on an unlabeled test set to obtain test-set-specific word representations. Through extensive experiments, we demonstrate that despite its simplicity, transductive LM fine-tuning consistently improves state-of-the-art neural models in in-domain and out-of-domain settings.

pdf bib
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments
Masato Hagiwara | Takumi Ito | Tatsuki Kuribayashi | Jun Suzuki | Kentaro Inui
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

Language technologies play a key role in assisting people with their writing. Although there has been steady progress in e.g., grammatical error correction (GEC), human writers are yet to benefit from this progress due to the high development cost of integrating with writing software. We propose TEASPN, a protocol and an open-source framework for achieving integrated writing assistance environments. The protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost. As a result, users can enjoy the integrated experience in their favorite writing software. The results from experiments with human participants show that users use a wide range of technologies and rate their writing experience favorably, allowing them to write more fluent text.

pdf bib
When Choosing Plausible Alternatives, Clever Hans can be Clever
Pride Kavumba | Naoya Inoue | Benjamin Heinzerling | Keshav Singh | Paul Reisert | Kentaro Inui
Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing

Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA. However, recent work found that many improvements in benchmarks of natural language understanding are not due to models learning the task, but due to their increasing ability to exploit superficial cues, such as tokens that occur more often in the correct answer than the wrong one. Are BERT’s and RoBERTa’s good performance on COPA also caused by this? We find superficial cues in COPA, as well as evidence that BERT exploits these cues.To remedy this problem, we introduce Balanced COPA, an extension of COPA that does not suffer from easy-to-exploit single token cues. We analyze BERT’s and RoBERTa’s performance on original and Balanced COPA, finding that BERT relies on superficial cues when they are present, but still achieves comparable performance once they are made ineffective, suggesting that BERT learns the task to a certain degree when forced to. In contrast, RoBERTa does not appear to rely on superficial cues.

pdf bib
Inject Rubrics into Short Answer Grading System
Tianqi Wang | Naoya Inoue | Hiroki Ouchi | Tomoya Mizumoto | Kentaro Inui
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

Short Answer Grading (SAG) is a task of scoring students’ answers in examinations. Most existing SAG systems predict scores based only on the answers, including the model used as base line in this paper, which gives the-state-of-the-art performance. But they ignore important evaluation criteria such as rubrics, which play a crucial role for evaluating answers in real-world situations. In this paper, we present a method to inject information from rubrics into SAG systems. We implement our approach on top of word-level attention mechanism to introduce the rubric information, in order to locate information in each answer that are highly related to the score. Our experimental results demonstrate that injecting rubric information effectively contributes to the performance improvement and that our proposed model outperforms the state-of-the-art SAG model on the widely used ASAP-SAS dataset under low-resource settings.

pdf bib
Improving Evidence Detection by Leveraging Warrants
Keshav Singh | Paul Reisert | Naoya Inoue | Pride Kavumba | Kentaro Inui
Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)

Recognizing the implicit link between a claim and a piece of evidence (i.e. warrant) is the key to improving the performance of evidence detection. In this work, we explore the effectiveness of automatically extracted warrants for evidence detection. Given a claim and candidate evidence, our proposed method extracts multiple warrants via similarity search from an existing, structured corpus of arguments. We then attentively aggregate the extracted warrants, considering the consistency between the given argument and the acquired warrants. Although a qualitative analysis on the warrants shows that the extraction method needs to be improved, our results indicate that our method can still improve the performance of evidence detection.

pdf bib
An Empirical Study of Span Representations in Argumentation Structure Parsing
Tatsuki Kuribayashi | Hiroki Ouchi | Naoya Inoue | Paul Reisert | Toshinori Miyoshi | Jun Suzuki | Kentaro Inui
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

For several natural language processing (NLP) tasks, span representation design is attracting considerable attention as a promising new technique; a common basis for an effective design has been established. With such basis, exploring task-dependent extensions for argumentation structure parsing (ASP) becomes an interesting research direction. This study investigates (i) span representation originally developed for other NLP tasks and (ii) a simple task-dependent extension for ASP. Our extensive experiments and analysis show that these representations yield high performance for ASP and provide some challenging types of instances to be parsed.

pdf bib
Unsupervised Learning of Discourse-Aware Text Representation for Essay Scoring
Farjana Sultana Mim | Naoya Inoue | Paul Reisert | Hiroki Ouchi | Kentaro Inui
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Existing document embedding approaches mainly focus on capturing sequences of words in documents. However, some document classification and regression tasks such as essay scoring need to consider discourse structure of documents. Although some prior approaches consider this issue and utilize discourse structure of text for document classification, these approaches are dependent on computationally expensive parsers. In this paper, we propose an unsupervised approach to capture discourse structure in terms of coherence and cohesion for document embedding that does not require any expensive parser or annotation. Extrinsic evaluation results show that the document representation obtained from our approach improves the performance of essay Organization scoring and Argument Strength scoring.

pdf bib
Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention
Qin Dai | Naoya Inoue | Paul Reisert | Ryo Takahashi | Kentaro Inui
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

The increased demand for structured scientific knowledge has attracted considerable attention in extracting scientific relation from the ever growing scientific publications. Distant supervision is widely applied approach to automatically generate large amounts of labelled data with low manual annotation cost. However, distant supervision inevitably accompanies the wrong labelling problem, which will negatively affect the performance of Relation Extraction (RE). To address this issue, (Han et al., 2018) proposes a novel framework for jointly training both RE model and Knowledge Graph Completion (KGC) model to extract structured knowledge from non-scientific dataset. In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset. Secondly, to achieve better performance on the biomedical dataset, we extend the framework with other competitive KGC models. Moreover, we proposed a new end-to-end KGC model to extend the framework. Experimental results not only show the feasibility of the framework on the biomedical dataset, but also indicate the effectiveness of our extensions, because our extended model achieves significant and consistent improvements on distant supervised RE as compared with baselines.

pdf bib
Annotating with Pros and Cons of Technologies in Computer Science Papers
Hono Shirai | Naoya Inoue | Jun Suzuki | Kentaro Inui
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

This paper explores a task for extracting a technological expression and its pros/cons from computer science papers. We report ongoing efforts on an annotated corpus of pros/cons and an analysis of the nature of the automatic extraction task. Specifically, we show how to adapt the targeted sentiment analysis task for pros/cons extraction in computer science papers and conduct an annotation study. In order to identify the challenges of the automatic extraction task, we construct a strong baseline model and conduct an error analysis. The experiments show that pros/cons can be consistently annotated by several annotators, and that the task is challenging due to domain-specific knowledge. The annotated dataset is made publicly available for research purposes.

pdf bib
Analytic Score Prediction and Justification Identification in Automated Short Answer Scoring
Tomoya Mizumoto | Hiroki Ouchi | Yoriko Isobe | Paul Reisert | Ryo Nagata | Satoshi Sekine | Kentaro Inui
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

This paper provides an analytical assessment of student short answer responses with a view to potential benefits in pedagogical contexts. We first propose and formalize two novel analytical assessment tasks: analytic score prediction and justification identification, and then provide the first dataset created for analytic short answer scoring research. Subsequently, we present a neural baseline model and report our extensive empirical results to demonstrate how our dataset can be used to explore new and intriguing technical challenges in short answer scoring. The dataset is publicly available for research purposes.

pdf bib
Can Neural Networks Understand Monotonicity Reasoning?
Hitomi Yanaka | Koji Mineshima | Daisuke Bekki | Kentaro Inui | Satoshi Sekine | Lasha Abzianidze | Johan Bos
Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures. Since no test set has been developed for monotonicity reasoning with wide coverage, it is still unclear whether neural models can perform monotonicity reasoning in a proper way. To investigate this issue, we introduce the Monotonicity Entailment Dataset (MED). Performance by state-of-the-art NLI models on the new test set is substantially worse, under 55%, especially on downward reasoning. In addition, analysis using a monotonicity-driven data augmentation method showed that these models might be limited in their generalization ability in upward and downward reasoning.

pdf bib
Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance
Takumi Ito | Tatsuki Kuribayashi | Hayato Kobayashi | Ana Brassard | Masato Hagiwara | Jun Suzuki | Kentaro Inui
Proceedings of the 12th International Conference on Natural Language Generation

The writing process consists of several stages such as drafting, revising, editing, and proofreading. Studies on writing assistance, such as grammatical error correction (GEC), have mainly focused on sentence editing and proofreading, where surface-level issues such as typographical errors, spelling errors, or grammatical errors should be corrected. We broaden this focus to include the earlier revising stage, where sentences require adjustment to the information included or major rewriting and propose Sentence-level Revision (SentRev) as a new writing assistance task. Well-performing systems in this task can help inexperienced authors by producing fluent, complete sentences given their rough, incomplete drafts. We build a new freely available crowdsourced evaluation dataset consisting of incomplete sentences authored by non-native writers paired with their final versions extracted from published academic papers for developing and evaluating SentRev models. We also establish baseline performance on SentRev using our newly built evaluation dataset.

pdf bib
A Large-Scale Multi-Length Headline Corpus for Analyzing Length-Constrained Headline Generation Model Evaluation
Yuta Hitomi | Yuya Taguchi | Hideaki Tamori | Ko Kikuta | Jiro Nishitoba | Naoaki Okazaki | Kentaro Inui | Manabu Okumura
Proceedings of the 12th International Conference on Natural Language Generation

Browsing news articles on multiple devices is now possible. The lengths of news article headlines have precise upper bounds, dictated by the size of the display of the relevant device or interface. Therefore, controlling the length of headlines is essential when applying the task of headline generation to news production. However, because there is no corpus of headlines of multiple lengths for a given article, previous research on controlling output length in headline generation has not discussed whether the system outputs could be adequately evaluated without multiple references of different lengths. In this paper, we introduce two corpora, which are Japanese News Corpus (JNC) and JApanese MUlti-Length Headline Corpus (JAMUL), to confirm the validity of previous evaluation settings. The JNC provides common supervision data for headline generation. The JAMUL is a large-scale evaluation dataset for headlines of three different lengths composed by professional editors. We report new findings on these corpora; for example, although the longest length reference summary can appropriately evaluate the existing methods controlling output length, this evaluation setting has several problems.

pdf bib
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough?
Masato Mita | Tomoya Mizumoto | Masahiro Kaneko | Ryo Nagata | Kentaro Inui
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

This 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 benchmark. However, the evaluation remains incomplete because the task difficulty varies depending on the test corpus and conditions such as the proficiency levels of the writers and essay topics. To overcome this limitation, we evaluate the performance of several GEC models, including NMT-based (LSTM, CNN, and transformer) and an SMT-based model, against various learner corpora (CoNLL-2013, CoNLL-2014, FCE, JFLEG, ICNALE, and KJ). Evaluation results reveal that the models’ rankings considerably vary depending on the corpus, indicating that single-corpus evaluation is insufficient for GEC models.

pdf bib
Subword-based Compact Reconstruction of Word Embeddings
Shota Sasaki | Jun Suzuki | Kentaro Inui
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

The idea of subword-based word embeddings has been proposed in the literature, mainly for solving the out-of-vocabulary (OOV) word problem observed in standard word-based word embeddings. In this paper, we propose a method of reconstructing pre-trained word embeddings using subword information that can effectively represent a large number of subword embeddings in a considerably small fixed space. The key techniques of our method are twofold: memory-shared embeddings and a variant of the key-value-query self-attention mechanism. Our experiments show that our reconstructed subword-based embeddings can successfully imitate well-trained word embeddings in a small fixed space while preventing quality degradation across several linguistic benchmark datasets, and can simultaneously predict effective embeddings of OOV words. We also demonstrate the effectiveness of our reconstruction method when we apply them to downstream tasks.

pdf bib
HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning
Hitomi Yanaka | Koji Mineshima | Daisuke Bekki | Kentaro Inui | Satoshi Sekine | Lasha Abzianidze | Johan Bos
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Large crowdsourced datasets are widely used for training and evaluating neural models on natural language inference (NLI). Despite these efforts, neural models have a hard time capturing logical inferences, including those licensed by phrase replacements, so-called monotonicity reasoning. Since no large dataset has been developed for monotonicity reasoning, it is still unclear whether the main obstacle is the size of datasets or the model architectures themselves. To investigate this issue, we introduce a new dataset, called HELP, for handling entailments with lexical and logical phenomena. We add it to training data for the state-of-the-art neural models and evaluate them on test sets for monotonicity phenomena. The results showed that our data augmentation improved the overall accuracy. We also find that the improvement is better on monotonicity inferences with lexical replacements than on downward inferences with disjunction and modification. This suggests that some types of inferences can be improved by our data augmentation while others are immune to it.

pdf bib
The Sally Smedley Hyperpartisan News Detector at SemEval-2019 Task 4
Kazuaki Hanawa | Shota Sasaki | Hiroki Ouchi | Jun Suzuki | Kentaro Inui
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes our system submitted to the formal run of SemEval-2019 Task 4: Hyperpartisan news detection. Our system is based on a linear classifier using several features, i.e., 1) embedding features based on the pre-trained BERT embeddings, 2) article length features, and 3) embedding features of informative phrases extracted from by-publisher dataset. Our system achieved 80.9% accuracy on the test set for the formal run and got the 3rd place out of 42 teams.

2018

pdf bib
A Melody-Conditioned Lyrics Language Model
Kento Watanabe | Yuichiroh Matsubayashi | Satoru Fukayama | Masataka Goto | Kentaro Inui | Tomoyasu Nakano
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody. Previously proposed models for lyrics generation suffer from the inability of capturing the relationship between lyrics and melody partly due to the unavailability of lyrics-melody aligned data. In this study, we first propose a new practical method for creating a large collection of lyrics-melody aligned data and then create a collection of 1,000 lyrics-melody pairs augmented with precise syllable-note alignments and word/sentence/paragraph boundaries. We then provide a quantitative analysis of the correlation between word/sentence/paragraph boundaries in lyrics and melodies. We then propose an RNN-based lyrics language model conditioned on a featurized melody. Experimental results show that the proposed model generates fluent lyrics while maintaining the compatibility between boundaries of lyrics and melody structures.

pdf bib
Cross-Lingual Learning-to-Rank with Shared Representations
Shota Sasaki | Shuo Sun | Shigehiko Schamoni | Kevin Duh | Kentaro Inui
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user’s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the data requirement. This model can exploit training data in, for example, Japanese-English CLIR to improve the results of Swahili-English CLIR.

pdf bib
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions
Sho Yokoi | Sosuke Kobayashi | Kenji Fukumizu | Jun Suzuki | Kentaro Inui
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In this paper, we propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions (e.g., sentences) with a very short learning time, as an alternative to pointwise mutual information (PMI). As well as deriving PMI from mutual information, we derive this new measure from the Hilbert–Schmidt independence criterion (HSIC); thus, we call the new measure the pointwise HSIC (PHSIC). PHSIC can be interpreted as a smoothed variant of PMI that allows various similarity metrics (e.g., sentence embeddings) to be plugged in as kernels. Moreover, PHSIC can be estimated by simple and fast (linear in the size of the data) matrix calculations regardless of whether we use linear or nonlinear kernels. Empirically, in a dialogue response selection task, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. In addition, we also demonstrate that PHSIC is beneficial as a criterion of a data selection task for machine translation owing to its ability to give high (low) scores to a consistent (inconsistent) pair with other pairs.

pdf bib
What Makes Reading Comprehension Questions Easier?
Saku Sugawara | Kentaro Inui | Satoshi Sekine | Akiko Aizawa
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

A challenge in creating a dataset for machine reading comprehension (MRC) is to collect questions that require a sophisticated understanding of language to answer beyond using superficial cues. In this work, we investigate what makes questions easier across recent 12 MRC datasets with three question styles (answer extraction, description, and multiple choice). We propose to employ simple heuristics to split each dataset into easy and hard subsets and examine the performance of two baseline models for each of the subsets. We then manually annotate questions sampled from each subset with both validity and requisite reasoning skills to investigate which skills explain the difference between easy and hard questions. From this study, we observed that (i) the baseline performances for the hard subsets remarkably degrade compared to those of entire datasets, (ii) hard questions require knowledge inference and multiple-sentence reasoning in comparison with easy questions, and (iii) multiple-choice questions tend to require a broader range of reasoning skills than answer extraction and description questions. These results suggest that one might overestimate recent advances in MRC.

pdf bib
Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis
Yuichiroh Matsubayashi | Kentaro Inui
Proceedings of the 27th International Conference on Computational Linguistics

Capturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese. In this paper, we propose new Japanese PAS analysis models that integrate the label prediction information of arguments in multiple PASs by extending the input and last layers of a standard deep bidirectional recurrent neural network (bi-RNN) model. In these models, using the mechanisms of pooling and attention, we aim to directly capture the potential interactions among multiple PASs, without being disturbed by the word order and distance. Our experiments show that the proposed models improve the prediction accuracy specifically for cases where the predicate and argument are in an indirect dependency relation and achieve a new state of the art in the overall F1 on a standard benchmark corpus.

pdf bib
Predicting Stances from Social Media Posts using Factorization Machines
Akira Sasaki | Kazuaki Hanawa | Naoaki Okazaki | Kentaro Inui
Proceedings of the 27th International Conference on Computational Linguistics

Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. An important step to analyze the discussions on social media and to assist in healthy decision-making is stance detection. This paper presents an approach to detect the stance of a user toward a topic based on their stances toward other topics and the social media posts of the user. We apply factorization machines, a widely used method in item recommendation, to model user preferences toward topics from the social media data. The experimental results demonstrate that users’ posts are useful to model topic preferences and therefore predict stances of silent users.

pdf bib
Interpretable and Compositional Relation Learning by Joint Training with an Autoencoder
Ryo Takahashi | Ran Tian | Kentaro Inui
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Embedding models for entities and relations are extremely useful for recovering missing facts in a knowledge base. Intuitively, a relation can be modeled by a matrix mapping entity vectors. However, relations reside on low dimension sub-manifolds in the parameter space of arbitrary matrices – for one reason, composition of two relations M1, M2 may match a third M3 (e.g. composition of relations currency_of_country and country_of_film usually matches currency_of_film_budget), which imposes compositional constraints to be satisfied by the parameters (i.e. M1*M2=M3). In this paper we investigate a dimension reduction technique by training relations jointly with an autoencoder, which is expected to better capture compositional constraints. We achieve state-of-the-art on Knowledge Base Completion tasks with strongly improved Mean Rank, and show that joint training with an autoencoder leads to interpretable sparse codings of relations, helps discovering compositional constraints and benefits from compositional training. Our source code is released at github.com/tianran/glimvec.

pdf bib
Unsupervised Learning of Style-sensitive Word Vectors
Reina Akama | Kento Watanabe | Sho Yokoi | Sosuke Kobayashi | Kentaro Inui
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

This paper presents the first study aimed at capturing stylistic similarity between words in an unsupervised manner. We propose extending the continuous bag of words (CBOW) embedding model (Mikolov et al., 2013b) to learn style-sensitive word vectors using a wider context window under the assumption that the style of all the words in an utterance is consistent. In addition, we introduce a novel task to predict lexical stylistic similarity and to create a benchmark dataset for this task. Our experiment with this dataset supports our assumption and demonstrates that the proposed extensions contribute to the acquisition of style-sensitive word embeddings.

pdf bib
Feasible Annotation Scheme for Capturing Policy Argument Reasoning using Argument Templates
Paul Reisert | Naoya Inoue | Tatsuki Kuribayashi | Kentaro Inui
Proceedings of the 5th Workshop on Argument Mining

Most of the existing works on argument mining cast the problem of argumentative structure identification as classification tasks (e.g. attack-support relations, stance, explicit premise/claim). This paper goes a step further by addressing the task of automatically identifying reasoning patterns of arguments using predefined templates, which is called argument template (AT) instantiation. The contributions of this work are three-fold. First, we develop a simple, yet expressive set of easily annotatable ATs that can represent a majority of writer’s reasoning for texts with diverse policy topics while maintaining the computational feasibility of the task. Second, we create a small, but highly reliable annotated corpus of instantiated ATs on top of reliably annotated support and attack relations and conduct an annotation study. Third, we formulate the task of AT instantiation as structured prediction constrained by a feasible set of templates. Our evaluation demonstrates that we can annotate ATs with a reasonably high inter-annotator agreement, and the use of template-constrained inference is useful for instantiating ATs with only partial reasoning comprehension clues.

pdf bib
Unsupervised Token-wise Alignment to Improve Interpretation of Encoder-Decoder Models
Shun Kiyono | Sho Takase | Jun Suzuki | Naoaki Okazaki | Kentaro Inui | Masaaki Nagata
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Developing a method for understanding the inner workings of black-box neural methods is an important research endeavor. Conventionally, many studies have used an attention matrix to interpret how Encoder-Decoder-based models translate a given source sentence to the corresponding target sentence. However, recent studies have empirically revealed that an attention matrix is not optimal for token-wise translation analyses. We propose a method that explicitly models the token-wise alignment between the source and target sequences to provide a better analysis. Experiments show that our method can acquire token-wise alignments that are superior to those of an attention mechanism.

pdf bib
Investigating the Challenges of Temporal Relation Extraction from Clinical Text
Diana Galvan | Naoaki Okazaki | Koji Matsuda | Kentaro Inui
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain. The complexity of temporal representation in language is evident as results of the 2016 Clinical TempEval challenge indicate: the current state-of-the-art systems perform well in solving mention-identification tasks of event and time expressions but poorly in temporal relation extraction, showing a gap of around 0.25 point below human performance. We explore to adapt the tree-based LSTM-RNN model proposed by Miwa and Bansal (2016) to temporal relation extraction from clinical text, obtaining a five point improvement over the best 2016 Clinical TempEval system and two points over the state-of-the-art. We deliver a deep analysis of the results and discuss the next step towards human-like temporal reasoning.

pdf bib
Multi-dialect Neural Machine Translation and Dialectometry
Kaori Abe | Yuichiroh Matsubayashi | Naoaki Okazaki | Kentaro Inui
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

pdf bib
Improving Scientific Relation Classification with Task Specific Supersense
Qin Dai | Naoya Inoue | Paul Reisert | Kentaro Inui
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

pdf bib
Reducing Odd Generation from Neural Headline Generation
Shun Kiyono | Sho Takase | Jun Suzuki | Naoaki Okazaki | Kentaro Inui | Masaaki Nagata
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

pdf bib
Suspicious News Detection Using Micro Blog Text
Tsubasa Tagami | Hiroki Ouchi | Hiroki Asano | Kazuaki Hanawa | Kaori Uchiyama | Kaito Suzuki | Kentaro Inui | Atsushi Komiya | Atsuo Fujimura | Ryo Yamashita | Hitofumi Yanai | Akinori Machino
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

2017

pdf bib
Other Topics You May Also Agree or Disagree: Modeling Inter-Topic Preferences using Tweets and Matrix Factorization
Akira Sasaki | Kazuaki Hanawa | Naoaki Okazaki | Kentaro Inui
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We presents in this paper our approach for modeling inter-topic preferences of Twitter users: for example, “those who agree with the Trans-Pacific Partnership (TPP) also agree with free trade”. This kind of knowledge is useful not only for stance detection across multiple topics but also for various real-world applications including public opinion survey, electoral prediction, electoral campaigns, and online debates. In order to extract users’ preferences on Twitter, we design linguistic patterns in which people agree and disagree about specific topics (e.g., “A is completely wrong”). By applying these linguistic patterns to a collection of tweets, we extract statements agreeing and disagreeing with various topics. Inspired by previous work on item recommendation, we formalize the task of modeling inter-topic preferences as matrix factorization: representing users’ preference as a user-topic matrix and mapping both users and topics onto a latent feature space that abstracts the preferences. Our experimental results demonstrate both that our presented approach is useful in predicting missing preferences of users and that the latent vector representations of topics successfully encode inter-topic preferences.

pdf bib
Neural Architectures for Fine-grained Entity Type Classification
Sonse Shimaoka | Pontus Stenetorp | Kentaro Inui | Sebastian Riedel
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In this work, we investigate several neural network architectures for fine-grained entity type classification and make three key contributions. Despite being a natural comparison and addition, previous work on attentive neural architectures have not considered hand-crafted features and we combine these with learnt features and establish that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism learns to attend over syntactic heads and the phrase containing the mention, both of which are known to be strong hand-crafted features for our task. We introduce parameter sharing between labels through a hierarchical encoding method, that in low-dimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We demonstrate that the choice of training data has a drastic impact on performance, which decreases by as much as 9.85% loose micro F1 score for a previously proposed method. Despite this discrepancy, our best model achieves state-of-the-art results with 75.36% loose micro F1 score on the well-established Figer (GOLD) dataset and we report the best results for models trained using publicly available data for the OntoNotes dataset with 64.93% loose micro F1 score.

pdf bib
A Neural Language Model for Dynamically Representing the Meanings of Unknown Words and Entities in a Discourse
Sosuke Kobayashi | Naoaki Okazaki | Kentaro Inui
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

This study addresses the problem of identifying the meaning of unknown words or entities in a discourse with respect to the word embedding approaches used in neural language models. We proposed a method for on-the-fly construction and exploitation of word embeddings in both the input and output layers of a neural model by tracking contexts. This extends the dynamic entity representation used in Kobayashi et al. (2016) and incorporates a copy mechanism proposed independently by Gu et al. (2016) and Gulcehre et al. (2016). In addition, we construct a new task and dataset called Anonymized Language Modeling for evaluating the ability to capture word meanings while reading. Experiments conducted using our novel dataset show that the proposed variant of RNN language model outperformed the baseline model. Furthermore, the experiments also demonstrate that dynamic updates of an output layer help a model predict reappearing entities, whereas those of an input layer are effective to predict words following reappearing entities.

pdf bib
Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
Yuichiroh Matsubayashi | Kentaro Inui
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions. However, the existing global models tend to employ only relatively simple local features; therefore, the overall performance gains are rather limited. The importance of designing a local model is demonstrated in this study by showing that the performance of a sophisticated local model can be considerably improved with recent feature embedding methods and a feature combination learning based on a neural network, outperforming the state-of-the-art global models in F1 on a common benchmark dataset.

pdf bib
Reference-based Metrics can be Replaced with Reference-less Metrics in Evaluating Grammatical Error Correction Systems
Hiroki Asano | Tomoya Mizumoto | Kentaro Inui
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

In grammatical error correction (GEC), automatically evaluating system outputs requires gold-standard references, which must be created manually and thus tend to be both expensive and limited in coverage. To address this problem, a reference-less approach has recently emerged; however, previous reference-less metrics that only consider the criterion of grammaticality, have not worked as well as reference-based metrics. This study explores the potential of extending a prior grammaticality-based method to establish a reference-less evaluation method for GEC systems. Further, we empirically show that a reference-less metric that combines fluency and meaning preservation with grammaticality provides a better estimate of manual scores than that of commonly used reference-based metrics. To our knowledge, this is the first study that provides empirical evidence that a reference-less metric can replace reference-based metrics in evaluating GEC systems.

pdf bib
Generating Stylistically Consistent Dialog Responses with Transfer Learning
Reina Akama | Kazuaki Inada | Naoya Inoue | Sosuke Kobayashi | Kentaro Inui
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We propose a novel, data-driven, and stylistically consistent dialog response generation system. To create a user-friendly system, it is crucial to make generated responses not only appropriate but also stylistically consistent. For leaning both the properties effectively, our proposed framework has two training stages inspired by transfer learning. First, we train the model to generate appropriate responses, and then we ensure that the responses have a specific style. Experimental results demonstrate that the proposed method produces stylistically consistent responses while maintaining the appropriateness of the responses learned in a general domain.

pdf bib
Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction
Yuta Hitomi | Hideaki Tamori | Naoaki Okazaki | Kentaro Inui
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

This paper explores the idea of robot editors, automated proofreaders that enable journalists to improve the quality of their articles. We propose a novel neural model of multi-task learning that both generates proofread sentences and predicts the editing operations required to rewrite the source sentences and create the proofread ones. The model is trained using logs of the revisions made professional editors revising draft newspaper articles written by journalists. Experiments demonstrate the effectiveness of our multi-task learning approach and the potential value of using revision logs for this task.

pdf bib
A Crowdsourcing Approach for Annotating Causal Relation Instances in Wikipedia
Kazuaki Hanawa | Akira Sasaki | Naoaki Okazaki | Kentaro Inui
Proceedings of the 31st Pacific Asia Conference on Language, Information and Computation

pdf bib
Analyzing the Revision Logs of a Japanese Newspaper for Article Quality Assessment
Hideaki Tamori | Yuta Hitomi | Naoaki Okazaki | Kentaro Inui
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

We address the issue of the quality of journalism and analyze daily article revision logs from a Japanese newspaper company. The revision logs contain data that can help reveal the requirements of quality journalism such as the types and number of edit operations and aspects commonly focused in revision. This study also discusses potential applications such as quality assessment and automatic article revision as our future research directions.

pdf bib
Handling Multiword Expressions in Causality Estimation
Shota Sasaki | Sho Takase | Naoya Inoue | Naoaki Okazaki | Kentaro Inui
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

2016

pdf bib
Question-Answering with Logic Specific to Video Games
Corentin Dumont | Ran Tian | Kentaro Inui
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present a corpus and a knowledge database aiming at developing Question-Answering in a new context, the open world of a video game. We chose a popular game called ‘Minecraft’, and created a QA corpus with a knowledge database related to this game and the ontology of a meaning representation that will be used to structure this database. We are interested in the logic rules specific to the game, which may not exist in the real world. The ultimate goal of this research is to build a QA system that can answer natural language questions from players by using inference on these game-specific logic rules. The QA corpus is partially composed of online quiz questions and partially composed of manually written variations of the most relevant ones. The knowledge database is extracted from several wiki-like websites about Minecraft. It is composed of unstructured data, such as text, that will be structured using the meaning representation we defined, and already structured data such as infoboxes. A preliminary examination of the data shows that players are asking creative questions about the game, and that the QA corpus can be used for clustering verbs and linking them to predefined actions in the game.

pdf bib
Dynamic Entity Representation with Max-pooling Improves Machine Reading
Sosuke Kobayashi | Ran Tian | Naoaki Okazaki | Kentaro Inui
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Modeling Discourse Segments in Lyrics Using Repeated Patterns
Kento Watanabe | Yuichiroh Matsubayashi | Naho Orita | Naoaki Okazaki | Kentaro Inui | Satoru Fukayama | Tomoyasu Nakano | Jordan Smith | Masataka Goto
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics. To test our hypothesis that discourse segmentations in lyrics strongly correlate with repeated patterns, we conduct the first large-scale corpus study on discourse segments in lyrics. Next, we propose the task to automatically identify segment boundaries in lyrics and train a logistic regression model for the task with the repeated pattern and textual features. The results of our empirical experiments illustrate the significance of capturing repeated patterns in predicting the boundaries of discourse segments in lyrics.

pdf bib
Modeling Context-sensitive Selectional Preference with Distributed Representations
Naoya Inoue | Yuichiroh Matsubayashi | Masayuki Ono | Naoaki Okazaki | Kentaro Inui
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP). CSP models the narrative consistency between the predicate and preceding contexts of its arguments, in addition to the conventional SP based on semantic types. Furthermore, we present a novel CSP model that extends the neural SP model (Van de Cruys, 2014) to incorporate contextual information into the distributed representations of arguments. Experimental results demonstrate that the proposed CSP model successfully learns CSP and outperforms the conventional SP model in coreference cluster ranking.

pdf bib
Learning Semantically and Additively Compositional Distributional Representations
Ran Tian | Naoaki Okazaki | Kentaro Inui
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Composing Distributed Representations of Relational Patterns
Sho Takase | Naoaki Okazaki | Kentaro Inui
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Building a Corpus for Japanese Wikification with Fine-Grained Entity Classes
Davaajav Jargalsaikhan | Naoaki Okazaki | Koji Matsuda | Kentaro Inui
Proceedings of the ACL 2016 Student Research Workshop

pdf bib
An Attentive Neural Architecture for Fine-grained Entity Type Classification
Sonse Shimaoka | Pontus Stenetorp | Kentaro Inui | Sebastian Riedel
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

pdf bib
Recognizing Open-Vocabulary Relations between Objects in Images
Masayasu Muraoka | Sumit Maharjan | Masaki Saito | Kota Yamaguchi | Naoaki Okazaki | Takayuki Okatani | Kentaro Inui
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers

pdf bib
Toward the automatic extraction of knowledge of usable goods
Mei Uemura | Naho Orita | Naoaki Okazaki | Kentaro Inui
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Oral Papers

pdf bib
Neural Joint Learning for Classifying Wikipedia Articles into Fine-grained Named Entity Types
Masatoshi Suzuki | Koji Matsuda | Satoshi Sekine | Naoaki Okazaki | Kentaro Inui
Proceedings of the 30th Pacific Asia Conference on Language, Information and Computation: Posters

pdf bib
Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection
Yuki Igarashi | Hiroya Komatsu | Sosuke Kobayashi | Naoaki Okazaki | Kentaro Inui
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

pdf bib
A Computational Approach for Generating Toulmin Model Argumentation
Paul Reisert | Naoya Inoue | Naoaki Okazaki | Kentaro Inui
Proceedings of the 2nd Workshop on Argumentation Mining

pdf bib
Learning Sentence Ordering for Opinion Generation of Debate
Toshihiko Yanase | Toshinori Miyoshi | Kohsuke Yanai | Misa Sato | Makoto Iwayama | Yoshiki Niwa | Paul Reisert | Kentaro Inui
Proceedings of the 2nd Workshop on Argumentation Mining

pdf bib
Semantic Annotation of Japanese Functional Expressions and its Impact on Factuality Analysis
Yudai Kamioka | Kazuya Narita | Junta Mizuno | Miwa Kanno | Kentaro Inui
Proceedings of The 9th Linguistic Annotation Workshop

pdf bib
Annotating Geographical Entities on Microblog Text
Koji Matsuda | Akira Sasaki | Naoaki Okazaki | Kentaro Inui
Proceedings of The 9th Linguistic Annotation Workshop

pdf bib
Fast and Large-scale Unsupervised Relation Extraction
Sho Takase | Naoaki Okazaki | Kentaro Inui
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

pdf bib
Reducing Lexical Features in Parsing by Word Embeddings
Hiroya Komatsu | Ran Tian | Naoaki Okazaki | Kentaro Inui
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

pdf bib
Recognizing Complex Negation on Twitter
Junta Mizuno | Canasai Kruengkrai | Kiyonori Ohtake | Chikara Hashimoto | Kentaro Torisawa | Julien Kloetzer | Kentaro Inui
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

2014

pdf bib
A Corpus Study for Identifying Evidence on Microblogs
Paul Reisert | Junta Mizuno | Miwa Kanno | Naoaki Okazaki | Kentaro Inui
Proceedings of LAW VIII - The 8th Linguistic Annotation Workshop

pdf bib
Finding The Best Model Among Representative Compositional Models
Masayasu Muraoka | Sonse Shimaoka | Kazeto Yamamoto | Yotaro Watanabe | Naoaki Okazaki | Kentaro Inui
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

pdf bib
An Example-Based Approach to Difficult Pronoun Resolution
Canasai Kruengkrai | Naoya Inoue | Jun Sugiura | Kentaro Inui
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

pdf bib
Modeling Structural Topic Transitions for Automatic Lyrics Generation
Kento Watanabe | Yuichiroh Matsubayashi | Kentaro Inui | Masataka Goto
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

2013

pdf bib
A Lexicon-based Investigation of Research Issues in Japanese Factuality Analysis
Kazuya Narita | Junta Mizuno | Kentaro Inui
Proceedings of the Sixth International Joint Conference on Natural Language Processing

pdf bib
NICT Disaster Information Analysis System
Kiyonori Ohtake | Jun Goto | Stijn De Saeger | Kentaro Torisawa | Junta Mizuno | Kentaro Inui
The Companion Volume of the Proceedings of IJCNLP 2013: System Demonstrations

pdf bib
Proceedings of the Workshop on Language Processing and Crisis Information 2013
Kentaro Inui | Hideto Kazawa | Graham Neubig | Masao Utiyama
Proceedings of the Workshop on Language Processing and Crisis Information 2013

pdf bib
Computer-assisted Structuring of Emergency Management Information: A Project Note
Yotaro Watanabe | Kentaro Inui | Shingo Suzuki | Hiroko Koumoto | Mitsuhiro Higashida | Yuji Maeda | Katsumi Iwatsuki
Proceedings of the Workshop on Language Processing and Crisis Information 2013

pdf bib
Extracting and Aggregating False Information from Microblogs
Naoaki Okazaki | Keita Nabeshima | Kento Watanabe | Junta Mizuno | Kentaro Inui
Proceedings of the Workshop on Language Processing and Crisis Information 2013

pdf bib
Is a 204 cm Man Tall or Small ? Acquisition of Numerical Common Sense from the Web
Katsuma Narisawa | Yotaro Watanabe | Junta Mizuno | Naoaki Okazaki | Kentaro Inui
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Detecting Chronic Critics Based on Sentiment Polarity and User’s Behavior in Social Media
Sho Takase | Akiko Murakami | Miki Enoki | Naoaki Okazaki | Kentaro Inui
51st Annual Meeting of the Association for Computational Linguistics Proceedings of the Student Research Workshop

2012

pdf bib
Set Expansion using Sibling Relations between Semantic Categories
Sho Takase | Naoaki Okazaki | Kentaro Inui
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation

pdf bib
Coreference Resolution with ILP-based Weighted Abduction
Naoya Inoue | Ekaterina Ovchinnikova | Kentaro Inui | Jerry Hobbs
Proceedings of COLING 2012

pdf bib
A Latent Discriminative Model for Compositional Entailment Relation Recognition using Natural Logic
Yotaro Watanabe | Junta Mizuno | Eric Nichols | Naoaki Okazaki | Kentaro Inui
Proceedings of COLING 2012

2011

pdf bib
Recognizing Confinement in Web Texts
Megumi Ohki | Eric Nichols | Suguru Matsuyoshi | Koji Murakami | Junta Mizuno | Shouko Masuda | Kentaro Inui | Yuji Matsumoto
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

2010

pdf bib
Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables
Tetsuji Nakagawa | Kentaro Inui | Sadao Kurohashi
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

pdf bib
A Thesaurus of Predicate-Argument Structure for Japanese Verbs to Deal with Granularity of Verb Meanings
Koichi Takeuchi | Kentaro Inui | Nao Takeuchi | Atsushi Fujita
Proceedings of the Eighth Workshop on Asian Language Resouces

pdf bib
Automatic Classification of Semantic Relations between Facts and Opinions
Koji Murakami | Eric Nichols | Junta Mizuno | Yotaro Watanabe | Hayato Goto | Megumi Ohki | Suguru Matsuyoshi | Kentaro Inui | Yuji Matsumoto
Proceedings of the Second Workshop on NLP Challenges in the Information Explosion Era (NLPIX 2010)

pdf bib
Identifying Contradictory and Contrastive Relations between Statements to Outline Web Information on a Given Topic
Daisuke Kawahara | Kentaro Inui | Sadao Kurohashi
Coling 2010: Posters

pdf bib
Annotating Event Mentions in Text with Modality, Focus, and Source Information
Suguru Matsuyoshi | Megumi Eguchi | Chitose Sao | Koji Murakami | Kentaro Inui | Yuji Matsumoto
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Many natural language processing tasks, including information extraction, question answering and recognizing textual entailment, require analysis of the polarity, focus of polarity, tense, aspect, mood and source of the event mentions in a text in addition to its predicate-argument structure analysis. We refer to modality, polarity and other associated information as extended modality. In this paper, we propose a new annotation scheme for representing the extended modality of event mentions in a sentence. Our extended modality consists of the following seven components: Source, Time, Conditional, Primary modality type, Actuality, Evaluation and Focus. We reviewed the literature about extended modality in Linguistics and Natural Language Processing (NLP) and defined appropriate labels of each component. In the proposed annotation scheme, information of extended modality of an event mention is summarized at the core predicate of the event mention for immediate use in NLP applications. We also report on the current progress of our manual annotation of a Japanese corpus of about 50,000 event mentions, showing a reasonably high ratio of inter-annotator agreement.

2009

pdf bib
Interpolated PLSI for Learning Plausible Verb Arguments
Hiram Calvo | Kentaro Inui | Yuji Matsumoto
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 2

pdf bib
Annotating Semantic Relations Combining Facts and Opinions
Koji Murakami | Shouko Masuda | Suguru Matsuyoshi | Eric Nichols | Kentaro Inui | Yuji Matsumoto
Proceedings of the Third Linguistic Annotation Workshop (LAW III)

pdf bib
Capturing Salience with a Trainable Cache Model for Zero-anaphora Resolution
Ryu Iida | Kentaro Inui | Yuji Matsumoto
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

pdf bib
WISDOM: A Web Information Credibility Analysis Systematic
Susumu Akamine | Daisuke Kawahara | Yoshikiyo Kato | Tetsuji Nakagawa | Kentaro Inui | Sadao Kurohashi | Yutaka Kidawara
Proceedings of the ACL-IJCNLP 2009 Software Demonstrations

2008

pdf bib
Acquiring Event Relation Knowledge by Learning Cooccurrence Patterns and Fertilizing Cooccurrence Samples with Verbal Nouns
Shuya Abe | Kentaro Inui | Yuji Matsumoto
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

pdf bib
Two-Phased Event Relation Acquisition: Coupling the Relation-Oriented and Argument-Oriented Approaches
Shuya Abe | Kentaro Inui | Yuji Matsumoto
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

pdf bib
Emotion Classification Using Massive Examples Extracted from the Web
Ryoko Tokuhisa | Kentaro Inui | Yuji Matsumoto
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

pdf bib
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing
Satoshi Sekine | Kentaro Inui | Ido Dagan | Bill Dolan | Danilo Giampiccolo | Bernardo Magnini
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing

pdf bib
Annotating a Japanese Text Corpus with Predicate-Argument and Coreference Relations
Ryu Iida | Mamoru Komachi | Kentaro Inui | Yuji Matsumoto
Proceedings of the Linguistic Annotation Workshop

pdf bib
Extracting Aspect-Evaluation and Aspect-Of Relations in Opinion Mining
Nozomi Kobayashi | Kentaro Inui | Yuji Matsumoto
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

pdf bib
Exploiting Syntactic Patterns as Clues in Zero-Anaphora Resolution
Ryu Iida | Kentaro Inui | Yuji Matsumoto
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

pdf bib
Augmenting a Semantic Verb Lexicon with a Large Scale Collection of Example Sentences
Kentaro Inui | Toru Hirano | Ryu Iida | Atsushi Fujita | Yuji Matsumoto
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

One of the crucial issues in semantic parsing is how to reduce costs of collecting a sufficiently large amount of labeled data. This paper presents a new approach to cost-saving annotation of example sentences with predicate-argument structure information, taking Japanese as a target language. In this scheme, a large collection of unlabeled examples are first clustered and selectively sampled, and for each sampled cluster, only one representative example is given a label by a human annotator. The advantages of this approach are empirically supported by the results of our preliminary experiments, where we use an existing similarity function and naive sampling strategy.

2005

pdf bib
Exploiting Lexical Conceptual Structure for Paraphrase Generation
Atsushi Fujita | Kentaro Inui | Yuji Matsumoto
Second International Joint Conference on Natural Language Processing: Full Papers

pdf bib
Opinion Extraction Using a Learning-Based Anaphora Resolution Technique
Nozomi Kobayashi | Ryu Iida | Kentaro Inui | Yuji Matsumoto
Companion Volume to the Proceedings of Conference including Posters/Demos and tutorial abstracts

pdf bib
A Class-oriented Approach to Building a Paraphrase Corpus
Atsushi Fujita | Kentaro Inui
Proceedings of the Third International Workshop on Paraphrasing (IWP2005)

2004

pdf bib
Paraphrasing of Japanese Light-verb Constructions Based on Lexical Conceptual Structure
Atsushi Fujita | Kentaro Furihata | Kentaro Inui | Yuji Matsumoto | Koichi Takeuchi
Proceedings of the Workshop on Multiword Expressions: Integrating Processing

2003

pdf bib
Text Simplification for Reading Assistance: A Project Note
Kentaro Inui | Atsushi Fujita | Tetsuro Takahashi | Ryu Iida | Tomoya Iwakura
Proceedings of the Second International Workshop on Paraphrasing

pdf bib
Incorporating Contextual Cues in Trainable Models for Coreference Resolution
Ryu Iida | Kentaro Inui | Hiroya Takamura | Yuji Matsumoto
Proceedings of the 2003 EACL Workshop on The Computational Treatment of Anaphora

2001

pdf bib
A Paraphrase-Based Exploration of Cohesiveness Criteria
Kentaro Inui | Masaru Nogami
Proceedings of the ACL 2001 Eighth European Workshop on Natural Language Generation (EWNLG)

2000

pdf bib
Committee-based Decision Making in Probabilistic Partial Parsing
Takashi Inui | Kentaro Inui
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics

1998

pdf bib
An Empirical Evaluation on Statistical Parsing of Japanese Sentences Using Lexical Association Statistics
Kiyoaki Shirai | Kentaro Inui | Takenobu Tokunaga | Hozumi Tanaka
Proceedings of the Third Conference on Empirical Methods for Natural Language Processing

pdf bib
Selective Sampling for Example-based Word Sense Disambiguation
Atsushi Fujii | Kentaro Inui | Takenobu Tokunaga | Hozumi Tanaka
Computational Linguistics, Volume 24, Number 4, December 1998

1996

pdf bib
To what extent does case contribute to verb sense disambiguation?
Atsushi Fujii | Kentaro Inui | Takenobu Tokunaga | Hozumi Tanaka
COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

pdf bib
The Internet a “natural” channel for language learning
Kentaro Inui
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics

pdf bib
The Internet a “natural” channel for language learning
Kentaro Inui
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics

pdf bib
Selective Sampling of Effective Example Sentence Sets for Word Sense Disambiguation
Atsushi Fujii | Kentaro Inui | Takenobu Tokunaga | Hozumi Tanaka
Fourth Workshop on Very Large Corpora

Search
Co-authors