Kentaro Inui
Professor of Natural Language Processing
Research interests
Professor Inui’s research encompasses a broad spectrum of NLP domains, primarily focusing on the computational modeling of semantics and discourse, knowledge-intensive reasoning for language comprehension, and trustworthiness in large language models. He also has a keen interest in the educational aspects of NLP applications, conducting research in areas such as explainable automated writing evaluation and argumentation diagnosis. Email
Before joining MBZUAI, Professor Inui led the Natural Language Processing Lab at Tohoku University, Japan, for 13 years. He also directed the Natural Language Understanding Team at the RIKEN Center for the Advanced Intelligence Project.
Professor Inui began his career as an Assistant Professor at Tokyo Institute of Technology in 1995. Subsequently, he served as an Associate Professor at Kyushu Institute of Technology and Nara Institute of Science and Technology and as a visiting researcher at the University of Sussex, before joining Tohoku University in 2010. During his career, Professsor Inui has served as the editor-in-chief of the Journal of Information Processing and the Journal of Natural Language Processing.
He also took on the role of General Chair for EMNLP-IJCNLP 2019 and is currently the Chairperson of the Association for Natural Language Processing in Japan.
- Ph.D. in computer science from the Tokyo Institute of Technology, Japan.
- Master of Engineering in computer science from the Tokyo Institute of Technology, Japan.
- Commendation for Science and Technology by the Minister of MEXT, Japan, 2022
- Google Focused Research Award, 2019
- Best Paper Award, Journal of Natural Language Processing, 2021
- Best Paper Award, Transactions of the Japanese Society for Artificial Intelligence, 2017
- Outstanding Paper Award, EACL 2017
- IBM Faculty Award, 2016
- Docomo Mobile Science Award, 2015
- Best Paper Award (Computation), PACLIC 2015
- Best Paper Award, AMT 2014
- 20th Anniversary Best Paper Award, Journal of Natural Language Processing, 2014
- Best Paper Award (First Place), Journal of Natural Language Processing, 2014
- Best Paper Award (First Place), Annual Meeting of the Association for Natural Language Processing, Japan, 2020, 2016, 2015, 2014
- Best Paper Award, Annual Meeting of the Association for Natural Language Processing, Japan, 2022, 2021, 2020, 2019, 2018, 2017, 2016
- Chairperson of the Japanese Association for Natural Language Processing, 2022-
- General Chair of EMNLP-IJCNLP, 2019
- Executive Committee Member of the Asian Federation of Natural Language Processing, 2019–2021
- Associate Member of the Science Council of Japan, 2018–
- Editor in Chief of the Journal of Natural Language Processing, 2018-2019
- Director of the NPO FactCheck Initiative Japan, 2017–
- Editor in Chief of the Journal of Information Processing, 2014-2015
- Editorial Board Member of Computational Linguistics, 2009-2011
- Hiroaki Funayama, Yuya Asazuma, Yuichiroh Matsubayashi, Tomoya Mizumoto, Jun Suzuki and Kentaro Inui. Reducing the Cost: Cross-Prompt Pre-Finetuning for Short Answer Scoring. The 24th International Conference on Artificial Intelligence in Education (AIED 2023), pp.78–89, 2023.
- Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi and Kentaro Inui. Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023), pp.1343-1354, 2023.
- Yuko Tanaka, Miwa Inuzuka, Hiromi Arai, Yoichi Takahashi, Minao Kukita and Kentaro Inui. Who Does Not Benefit from Fact-Checking Websites? A Psychological Characteristic Predicts the Selective Avoidance of Clicking Uncongenial Facts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI 2023), pp.1–17, 2023.
- Qin Dai, Benjamin Heinzerling, Kentaro Inui. Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), 15 pages, 2022.
- Tatsuki Kuribayashi, Yohei Oseki, Ana Brassard, Kentaro Inui. Context Limitations Make Neural Language Models More Human-Like. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), 16 pages, 2022.
- Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi and Kentaro Inui. Incorporating Residual and Normalization Layers into Analysis of Masked Language Models. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021), pp.4547–4568, 2021.