Faculty Profiles - MATSUBARA, Shigeki

Papers - MATSUBARA, Shigeki

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A New Representation for Span-based CCG Parsing Reviewed

Yoshihide Kato and Shigeki Matsubara

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP-2021) page： 10579 - 10584 2021.11

Natural Language Inference using Neural Network and Tableau Method Reviewed

Ayahito Saji, Daiki Takao, Yoshihide Kato and Shigeki Matsubara

Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation (PACLIC 35) page： 406 - 414 2021.11

同時通訳における順送り方略の適用に影響する起点言語の特徴 Reviewed

蔡仲煕, 笠浩一朗, 松原茂樹

通訳翻訳研究 Vol. 20 page： 25 - 40 2021.2

Word reordering and comma insertion integrated with shift-reduce dependency parsing Reviewed

Miyachi K., Ohno T., Matsubara S.

ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence Vol. 2 page： 1144 - 1150 2021.2

Language：Japanese Publishing type：Research paper (international conference proceedings) Publisher：ICAART 2021 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence

Japanese has widely recognized as relatively free word order language. However, since Japanese word order is not completely arbitrary and has some sort of preference, even native Japanese writers often write Japanese sentences that are grammatically well-formed but not easy to read. Furthermore, in Japanese sentences, a comma plays an important role in explicitly separating the constituents such as words and phrases. This paper proposes a method of word reordering and comma insertion for hard-to-read Japanese sentences so that they become easier to read, as basic technique. Our contribution is to show the feasibility of concurrently performing word reordering, comma insertion, and dependency parsing.

Scopus

A New Representation for Span-based CCG Parsing

Kato, Y; Matsubara, S

2021 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP 2021) page： 10579 - 10584 2021

Classification of URLs Citing Research Artifacts in Scholarly Documents based on Distributed Representations International journal

Tsunokake M., Matsubara S.

CEUR Workshop Proceedings of 2nd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE2021) collocated with ACM/IEEE Joint Conference on Digital Libraries (JCDL2021) Vol. 3004 page： 20 - 25 2021

ESTIMATING THE GENERATION TIMING OF RESPONSIVE UTTERANCES BY ACTIVE LISTENERS OF SPOKEN NARRATIVES

Ito, K; Murata, M; Ohno, T; Matsubara, S

2021 IEEE AUTOMATIC SPEECH RECOGNITION AND UNDERSTANDING WORKSHOP (ASRU) page： 495 - 502 2021

Language：English Publishing type：Research paper (scientific journal) Publisher：2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021 - Proceedings

Narrating is a fundamental human desire. In order to satisfy the desire, it is more effective to have multiple listeners listen to narratives than to have one listener does. Conversational agents, e.g., communication robots and smart speakers, are expected to listen to narratives, instead of human listeners. For such agents to be recognized as narrative listeners, it is necessary to generate responsive utterances at appropriate timings. This paper proposes a method to estimate the generation timings of responsive utterances by active listeners of spoken narratives. The proposed method estimates them by encoding acoustic and linguistic information extracted from narratives with a transformer-based approach. To evaluate the performance of the proposed method, an experiment was conducted using 131, 616 responsive utterances to narratives. The experimental results demonstrated that the proposed method was effective for the estimation and both the acoustic and linguistic information was useful.

DOI： 10.1109/ASRU51503.2021.9688132

Web of Science

Scopus

Natural Language Inference using Neural Network and Tableau Method

Saji A., Takao D., Kato Y., Matsubara S.

Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021 page： 303 - 311 2021

Language：English Publishing type：Research paper (scientific journal) Publisher：Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021

Natural language inference (NLI) is the task of identifying the inferential relation between a text pair. Neural-based approaches recently achieved high performance on the NLI task, but they cannot explain their reasoning processes. On the other hand, symbolic approaches have an advantage that the reasoning process is understandable to humans. This paper proposes a method to integrate a neural NLI model and a tableau proof system, the latter of which explains the reasoning processes. The standard tableau method consists of decomposing logical formulas by applying inferential rules, and checking whether or not there exists a valuation that satisfies the given constraints. Unlike the standard tableau method, our method adopts not logical formulas but dependency structures as its ingredients and uses a neural NLI model for the latter process. In order to analyze the behavior of a neural NLI model, we conducted an experiment on the neural NLI model alone and the proposed method using the SNLI corpus.

Scopus

A New Representation for Span-based CCG Parsing

Kato Y., Matsubara S.

EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings page： 10579 - 10584 2021

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing, Proceedings

This paper proposes a new representation for CCG derivations. CCG derivations are represented as trees whose nodes are labeled with categories strictly restricted by CCG rule schemata. This characteristic is not suitable for span-based parsing models because they predict node labels independently. In other words, span-based models may generate invalid CCG derivations that violate the rule schemata. Our proposed representation decomposes CCG derivations into several independent pieces and prevents the span-based parsing models from violating the schemata. Our experimental result shows that an off-the-shelf span-based parser with our representation is comparable with previous CCG parsers.

Scopus

Word Reordering and Comma Insertion Integrated with Shift-Reduce Dependency Parsing

Miyachi, K; Ohno, T; Matsubara, S

ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2 page： 1144 - 1150 2021

What Affects the Word Order of Target Language in Simultaneous Interpretation

Zhongxi Cai, Koichiro Ryu, and Shigeki Matsubara

2020 International Conference on Asian Language Processing, IALP 2020 page： 135 - 140 2020.12

Identification and Classification of Research Data Cited in Scholarly Papers

Tsunokake Masaya, Matsubara Shigeki

IEEJ Transactions on Electronics, Information and Systems Vol. 140 ( 12 ) page： 1357 - 1364 2020.12

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Institute of Electrical Engineers of Japan

<p>This paper proposes a method for identifying and classifying the research data cited in scholarly papers, aiming at automatic generation of metadata stored in data repository. This study focuses on URL citations in the scholarly papers. That is, the targets are to identify the URLs referring to the research data and to classify them into tool and data. The method is realized as a multi-class classification (tool/data/others). The method acquires the distributed representations of the URLs from the context around them, and uses them as the input feature. There exists an advantage in that the meanings of URLs can be given based on their surrounding words. This study adopts an approach of computing the meaning of the entire URL from those of the components of the URL. In order to evaluate the performance of the proposed method, experiments on URL classification were conducted. The scholarly papers included in the proceedings of the international conference were used as experimental data. Experimental results have shown the effectiveness of the proposed method for identifying and classifying URLs referring to research data.</p>

DOI： 10.1541/ieejeiss.140.1357

Scopus

CiNii Research

Parsing Gapping Constructions Based on Grammatical and Semantic Roles Reviewed

Yoshihide Kato, Shigeki Matsubara

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP-2020) page： 2747 - 2752 2020.11

Identification of Research Data References Based on Citation Contexts

Tomoki Ikoma, Shigeki Matsubara

Vol. 12504 LNCS page： 149 - 156 2020

Relation between degree of empathy for narrative speech and type of responsive utterance in attentive listening

Koichiro Ito, Masaki Murata, Tomohiro Ohno, Shigeki Matsubara

LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings page： 696 - 701 2020

Features of Source Language That Affect the Word Order of Target Language in Simultaneous Interpretation Reviewed

CAI Zhongxi, RYU Koichiro, MATSUBARA Shigeki

Interpreting and Translation Studies: The Journal of the Japan Association for Interpreting and Translation Studies Vol. 20 ( 0 ) page： 25 - 40 2020

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Japan Association for Interpreting and Translation Studies

Simultaneous interpretation has to be conducted to shorten delay and reduce working memory
load. Interpreters adopt a strategy that involves the generation of a target language in the word
order that is similar to that of the source language. To clarify how the interpreters decide the
word order in practice, we conduct a statistical study on the word order of simultaneous
interpretation, based on comparison with written translation. The word order comparisons
indicate that the strategy of maintaining the original word order is more likely to be adopted in
cases where the word order is not consistent in different translations. We analyze the features of
source languages, including dependencies of chunk pairs, length of post modifier, and distance
between two chunks, and confirm that such features affect the word-order decision in
simultaneous interpretation.

DOI： 10.50837/its.2002

CiNii Research

PTB graph parsing with tree approximation Reviewed International journal

Kato Y., Matsubara S.

ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference page： 5344 - 5349 2020

Authorship：Last author Language：English Publishing type：Research paper (international conference proceedings) Publisher：ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

The Penn Treebank (PTB) represents syntactic structures as graphs due to nonlocal dependencies. This paper proposes a method that approximates PTB graph-structured representations by trees. By our approximation method, we can reduce nonlocal dependency identification and constituency parsing into single tree-based parsing. An experimental result demonstrates that our approximation method with an off-the-shelf tree-based constituency parser significantly outperforms the previous methods in nonlocal dependency identification.

Scopus

What Affects the Word Order of Target Language in Simultaneous Interpretation

Cai Zhongxi, Ryu Koichiro, Matsubara Shigeki

2020 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP 2020) page： 135 - 140 2020

Relation between Degree of Empathy for Narrative Speech and Type of Responsive Utterance in Attentive Listening

Ito, K; Murata, M; Ohno, T; Matsubara, S

PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION (LREC 2020) page： 696 - 701 2020

Parsing Gapping Constructions Based on Grammatical and Semantic Roles

Kato, Y; Matsubara, S

PROCEEDINGS OF THE 2020 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING (EMNLP) page： 2747 - 2752 2020

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Papers - MATSUBARA, Shigeki