Faculty Profiles - MATSUBARA, Shigeki

Papers - MATSUBARA, Shigeki

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Utilization of Text Data for Response Timing Detection in Attentive Listening Reviewed

Yu Watanabe, Koichiro Ito, Shigeki Matsubara

Proceedings of Interspeech-2024 page： 3565 - 3569 2024.9

Estimating Metadata of Research Artifacts to Enhance their Findability Reviewed

Koichiro Ito, Shigeki Matsubara

Proceedings of 2024 IEEE 20th International Conference on e-Science (e-Science) page： 1 - 2 2024.9

On an Intermediate Task for Classifying URL Citations on Scholarly Papers Reviewed

Kazuhiro Wada, Masaya Tsunokake, Shigeki Matsubara

Proceeding of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING2024) 2024.5

Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset Reviewed

Asahi Yoshida, Yoshihide Kato, Shigeki Matsubara

Proceeding of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING2024) 2024.5

Responsive Utterance Generation to Show Disagreement to Narrative in Attentive Listening Reviewed

Ito Koichiro, Murata Masaki, Ohno Tomohiro, Matsubara Shigeki

Journal of Natural Language Processing Vol. 31 ( 1 ) page： 212 - 249 2024.3

Authorship：Last author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Association for Natural Language Processing

Conversational agents, such as communication robots, are expected to play a role in listening to narratives. To recognize these robots as the listeners, it is essential for them to have a function that indicates their attentive listening to a narrative. The basic response strategy in attentive listening involves generating responsive utterances that show acceptance. However, the narratives occasionally contain self-deprecation or modesty. In such cases, the listener must be able to produce a response that shows disagreement with the narrator's utterance, that is, a disagreement response. This study demonstrates the feasibility of generating appropriate disagreement responses. First, we define a method to tag the timing and expression of the disagreement response to narrative data in a non-real-time environment, and verify that the method enables to construct a corpus to which the disagreement response timing and response expression are comprehensively and stably assigned, respectively. Next, we implement detection and classification methods for disagreement response timing and expressions, respectively, based on a pre-trained transformer-based model and verify the feasibility of generating disagreement responses using the response corpus through experiments.

DOI： 10.5715/jnlp.31.212

CiNii Research

Re-Evaluating Syntax-Based Negation Scope Resolution

YOSHIDA Asahi, KATO Yoshihide, MATSUBARA Shigeki

IEICE Transactions on Information and Systems Vol. E107.D ( 1 ) page： 165 - 168 2024.1

Language：English Publishing type：Research paper (scientific journal) Publisher：The Institute of Electronics, Information and Communication Engineers

Negation scope resolution is the process of detecting the negated part of a sentence. Unlike the syntax-based approach employed in previous researches, state-of-the-art methods performed better without the explicit use of syntactic structure. This work revisits the syntax-based approach and re-evaluates the effectiveness of syntactic structure in negation scope resolution. We replace the parser utilized in the prior works with state-of-the-art parsers and modify the syntax-based heuristic rules. The experimental results demonstrate that the simple modifications enhance the performance of the prior syntax-based method to the same level as state-of-the-art end-to-end neural-based methods.

DOI： 10.1587/transinf.2023edl8050

Web of Science

Scopus

CiNii Research

Enhancing Findability and Searchability of Research Data: Metadata Conversion and Registration in Institutional Repositories Open Access

Nosé M., Shinbori A., Miyoshi Y., Hori T., Ohira T., Hashiba J., Naoe C., Gakiya R., Okamoto M., Sagara T., Aoki T., Matsubara S., Takahashi I., Hayashi H., Yamada K., Minamiyama Y., Tanaka Y., Abe S., Ueno S., Imajo S., Saito Y., Ashikita T., Hori Y., Shimizu T., Okamura N., Hirano K., Bargatze L.

Data Science Journal Vol. 23 2024

Citation-Worthy Detection of URL Citations in Scholarly Papers Open Access

Kazuhiro Wada, Masaya Tsunokake, Shigeki Matsubara

Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries (JCDL-2024) ( 28 ) page： 1 - 5 2024

On an Intermediate Task for Classifying URL Citations on Scholarly Papers

Wada, K; Tsunokake, M; Matsubara, S

PROCEEDINGS OF THE 2024 JOINT INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, LANGUAGE RESOURCES AND EVALUATION (LREC-COLING 2024) page： 12359 - 12369 2024

Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset Reviewed

加藤芳秀, 松原茂樹

Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation Vol. - page： 12093 - 12099 2024

Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset

Yoshida, A; Kato, Y; Matsubara, S

PROCEEDINGS OF THE 2024 JOINT INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, LANGUAGE RESOURCES AND EVALUATION (LREC-COLING 2024) page： 12093 - 12099 2024

Paper Recommendation Using Citation Contexts in Scholarly Documents Reviewed

Tomoki Ikoma, Shigeki Matsubara

Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation (PACLIC 37) 2023.12

Automatic Insertion of Commas and Linefeeds into Lecture Transcripts Based on Multi-task Learning Reviewed

Zhicheng Fang, Masaki Murata, Shigeki Matsubara:

Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation (PACLIC 37) 2023.12

Identifying Influential References in Scholarly Papers Using Citation Contexts Reviewed

Ikoma T., Matsubara S.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14458 page： 152 - 161 2023.12

On the Use of Language Models for Function Identification of Citations in Scholarly Papers Reviewed

Tomoki Ikoma and Shigeki Matsubara

Proceedings of the 2nd Workshop on Information Extraction from Scientific Publications (WIESP 2023) 2023.11

Revisiting Syntax-Based Approach in Negation Scope Resolution Reviewed

Asahi Yoshida, Yoshihide Kato and Shigeki Matsubara

Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM2023) page： 18 - 23 2023.7

Natural Language Inference Using Neural Network and Tableau Method Reviewed

Saji Ayahito, Takao Daiki, Kato Yoshihide, Matsubara Shigeki

Journal of Natural Language Processing Vol. 30 ( 2 ) page： 632 - 663 2023.6

Authorship：Corresponding author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Association for Natural Language Processing

Natural language inference (NLI) is the task of identifying the inferential relation between a text pair. In recent times, neural-based approaches have achieved high performance in NLI. However, they are unable to explain their reasoning processes. On the other hand, symbolic approaches have the advantage that their reasoning process is understandable to humans. This paper proposes a method for integrating a neural NLI model and the tableau proof system, with the latter explaining the reasoning processes. The standard tableau method decomposes logical formulas by applying inferential rules and checks for a valuation that satisfies the given constraints. Unlike the standard tableau method, the proposed method uses dependency structures as its components rather than logical formulas and employs a neural NLI model for the latter process. To analyze the behavior of our method, we conducted an experiment on the neural NLI model and the proposed method using SNLI corpus. In addition, we formalize our method model-theoretically and clarify the theoretical limitations of this method based on the formalization.

DOI： 10.5715/jnlp.30.632

Research data storage URL： https://nagoya.repo.nii.ac.jp/records/2003368

CiNii Research

Generation Timing Estimation of Responsive Utterances to Show Attentive Listening to Narrative Speech Reviewed

ITO Koichiro, MURATA Masaki, OHNO Tomohiro, MATSUBARA Shigeki

Vol. J106-A ( 3 ) page： 136 - 145 2023.3

Authorship：Last author Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Institute of Electronics, Information and Communication Engineers

Conversational agents, e.g., communication robots and smart speakers, are expected to listen to human narratives. For realizing such agents, it is necessary to generate responsive utterances at appropriate timings. This paper proposes a method to estimate the degree of appropriateness as generation timings of responsive utterances. The proposed method estimates the degree by encoding acoustic and linguistic narrative information with a transformer-based approach. To evaluate the performance, an experiment was conducted using 131,616 responsive utterances. The experimental results demonstrated that the proposed method was effective. Additionally, we confirmed that the degree of appropriateness was useful for response timing detection.

DOI： 10.14923/transfunj.2022hap0013

CiNii Research

Bottom-up Japanese Word Ordering using BERT Reviewed

Masato Yamazoe, Tomohiro Ohno and Shigeki Matsubara

Proceedings of the 15th International Conference on Agents and Artificial Intelligence (ICAART-2023) Vol. 3 page： 673 - 681 2023.2

Japanese Word Reordering based on Topological Sort Reviewed

Peng Sun, Tomohiro Ohno and Shigeki Matsubara

Proceedings of the 15th International Conference on Agents and Artificial Intelligence (ICAART-2023) Vol. 3 page： 768 - 775 2023.2

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Papers - MATSUBARA, Shigeki