Saku Sugawara

According to our database, Saku Sugawara authored at least 35 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs.

Akiko Aizawa, Eiji Aramaki, Bowen Chen, Fei Cheng, Hiroyuki Deguchi, Rintaro Enomoto, Kazuki Fujii, Kensuke Fukumoto, Takuya Fukushima, Namgi Han, Yuto Harada, Chikara Hashimoto, Tatsuya Hiraoka, Shohei Hisada, Sosuke Hosokawa, Lu Jie, Keisuke Kamata, Teruhito Kanazawa, Hiroki Kanezashi, Hiroshi Kataoka, Satoru Katsumata, Daisuke Kawahara, Seiya Kawano, Atsushi Keyaki, Keisuke Kiryu, Hirokazu Kiyomaru, Takashi Kodama, Takahiro Kubo, Yohei Kuga, Ryoma Kumon, Shuhei Kurita, Sadao Kurohashi, Conglong Li, Taiki Maekawa, Hiroshi Matsuda, Yusuke Miyao, Kentaro Mizuki, Sakae Mizuki, Yugo Murawaki, Ryo Nakamura, Taishi Nakamura, Kouta Nakayama, Tomoka Nakazato, Takuro Niitsuma, Jiro Nishitoba, Yusuke Oda, Hayato Ogawa, Takumi Okamoto, Naoaki Okazaki, Yohei Oseki, Shintaro Ozaki, Koki Ryu, Rafal Rzepka, Keisuke Sakaguchi, Shota Sasaki, Satoshi Sekine, Kohei Suda, Saku Sugawara, Issa Sugiura, Hiroaki Sugiyama, Hisami Suzuki, Jun Suzuki, Toyotaro Suzumura, Kensuke Tachibana, Yu Takagi, Kyosuke Takami, Koichi Takeda, Masashi Takeshita, Masahiro Tanaka, Kenjiro Taura, Arseny Tolmachev, Nobuhiro Ueda, Zhen Wan, Shuntaro Yada, Sakiko Yahata, Yuya Yamamoto, Yusuke Yamauchi, Hitomi Yanaka, Rio Yokota, Koichiro Yoshino

CoRR, 2024

MoreHopQA: More Than Multi-hop Reasoning.

Julian Schnitzler, Xanh Ho, Jiahao Huang, Florian Boudin, Saku Sugawara, Akiko Aizawa

CoRR, 2024

Can Language Models Induce Grammatical Knowledge from Indirect Evidence?

Miyu Oba, Yohei Oseki, Akiyo Fukatsu, Akari Haga, Hiroki Ouchi, Taro Watanabe, Saku Sugawara

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Rationale-Aware Answer Verification by Pairwise Self-Evaluation.

Akira Kawabata, Saku Sugawara

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Modeling Overregularization in Children with Small Language Models.

Akari Haga, Saku Sugawara, Akiyo Fukatsu, Miyu Oba, Hiroki Ouchi, Taro Watanabe, Yohei Oseki

Proceedings of the Findings of the Association for Computational Linguistics, 2024

What Makes Language Models Good-enough?

Daiki Asami, Saku Sugawara

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Information Extraction from Lengthy Legal Contracts: Leveraging Query-Based Summarization and GPT-3.5.

May Myo Zin, Ha-Thanh Nguyen, Ken Satoh, Saku Sugawara, Fumihito Nishino

Proceedings of the Legal Knowledge and Information Systems, 2023

Improving Translation of Case Descriptions into Logical Fact Formulas using LegalCaseNER.

May Myo Zin, Ha-Thanh Nguyen, Ken Satoh, Saku Sugawara, Fumihito Nishino

Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, 2023

Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension.

Akira Kawabata, Saku Sugawara

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering.

Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa

Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

PROPRES: Investigating the Projectivity of Presupposition with Various Triggers and Environments.

Daiki Asami, Saku Sugawara

Proceedings of the 27th Conference on Computational Natural Language Learning, 2023

On Degrees of Freedom in Defining and Testing Natural Language Understanding.

Saku Sugawara, Shun Tsugita

Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

Probing Physical Reasoning with Counter-Commonsense Context.

Kazushi Kondo, Saku Sugawara, Akiko Aizawa

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023

Which Shortcut Solution Do Question Answering Models Prefer to Learn?

Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering.

Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

CoRR, 2022

A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension.

Xanh Ho, Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa

CoRR, 2022

Interdisciplinarity Inference Using a Cross Encoder between Research Projects.

Teruhito Kanazawa, Hidekazu Nakawatase, Saku Sugawara, Chifumi Nishioka

Proceedings of the Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, 2022

How Well Do Multi-hop Reading Comprehension Models Understand Date Information?

Xanh Ho, Saku Sugawara, Akiko Aizawa

Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, 2022

Cross-Modal Similarity-Based Curriculum Learning for Image Captioning.

Hongkuan Zhang, Saku Sugawara, Akiko Aizawa, Lei Zhou, Ryohei Sasano, Koichi Takeda

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Debiasing Masks: A New Framework for Shortcut Mitigation in NLU.

Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios.

Mana Ashida, Saku Sugawara

Proceedings of the 29th International Conference on Computational Linguistics, 2022

Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering.

Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

Proceedings of the Fifth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2022

What Makes Reading Comprehension Questions Difficult?

Saku Sugawara, Nikita Nangia, Alex Warstadt, Samuel R. Bowman

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap.

Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

CoRR, 2021

Benchmarking Machine Reading Comprehension: A Psychological Perspective.

Saku Sugawara, Pontus Stenetorp, Akiko Aizawa

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021

Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation.

Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa

Proceedings of the ACL-IJCNLP 2021 Student Research Workshop, 2021

What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?

Nikita Nangia, Saku Sugawara, Harsh Trivedi, Alex Warstadt, Clara Vania, Samuel R. Bowman

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

Embracing Ambiguity: Shifting the Training Target of NLI Models.

Johannes Mario Meissner, Napat Thumwanit, Saku Sugawara, Akiko Aizawa

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

Prerequisites for Explainable Machine Reading Comprehension: A Position Paper.

Saku Sugawara, Pontus Stenetorp, Akiko Aizawa

CoRR, 2020

Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps.

Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa

Proceedings of the 28th International Conference on Computational Linguistics, 2020

Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets.

Saku Sugawara, Pontus Stenetorp, Kentaro Inui, Akiko Aizawa

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2018

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, Brussels, Belgium, October 31, 2018

2017

Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability.

Saku Sugawara, Yusuke Kido, Hikaru Yokono, Akiko Aizawa

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017

Prerequisite Skills for Reading Comprehension: Multi-Perspective Analysis of MCTest Datasets and Systems.

Saku Sugawara, Hikaru Yokono, Akiko Aizawa

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Annotation and Analysis of Discourse Relations, Temporal Relations and Multi-Layered Situational Relations in Japanese Texts.

Kimi Kaneko, Saku Sugawara, Koji Mineshima, Daisuke Bekki

Proceedings of the 12th Workshop on Asian Language Resources, 2016