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Saku Sugawara
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What Makes Reading Comprehension Questions Easier?
S Sugawara, K Inui, S Sekine, A Aizawa
Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018
1032018
Assessing the benchmarking capacity of machine reading comprehension datasets
S Sugawara, P Stenetorp, K Inui, A Aizawa
Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 8918-8927, 2020
612020
Evaluation metrics for machine reading comprehension: Prerequisite skills and readability
S Sugawara, Y Kido, H Yokono, A Aizawa
Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017
382017
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps
X Ho, AKD Nguyen, S Sugawara, A Aizawa
Proceedings of the 28th International Conference on Computational …, 2020
212020
Prerequisite skills for reading comprehension: Multi-perspective analysis of mctest datasets and systems
S Sugawara, H Yokono, A Aizawa
Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017
172017
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?
N Nangia, S Sugawara, H Trivedi, A Warstadt, C Vania, SR Bowman
Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021
142021
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation
K Shinoda, S Sugawara, A Aizawa
Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021
11*2021
Benchmarking Machine Reading Comprehension: A Psychological Perspective
S Sugawara, P Stenetorp, A Aizawa
Proceedings of the 16th Conference of the European Chapter of the …, 2021
11*2021
An analysis of prerequisite skills for reading comprehension
S Sugawara, A Aizawa
Proceedings of the Workshop on Uphill Battles in Language Processing …, 2016
102016
Embracing Ambiguity: Shifting the Training Target of NLI Models
JM Meissner, N Thumwanit, S Sugawara, A Aizawa
Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021
82021
What Makes Reading Comprehension Questions Difficult?
S Sugawara, N Nangia, A Warstadt, SR Bowman
Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022
42022
Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap
K Shinoda, S Sugawara, A Aizawa
Proceedings of the 3rd Workshop on Machine Reading for Question Answering, 63-72, 2021
32021
Annotation and Analysis of Discourse Relations, Temporal Relations and Multi-Layered Situational Relations in Japanese Texts
K Kaneko, S Sugawara, K Mineshima, D Bekki
Proceedings of the 12th Workshop on Asian Language Resources (ALR12), 10-19, 2016
22016
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning
H Zhang, S Sugawara, A Aizawa, L Zhou, R Sasano, K Takeda
arXiv preprint arXiv:2212.07075, 2022
2022
Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering
K Shinoda, S Sugawara, A Aizawa
arXiv preprint arXiv:2211.16093, 2022
2022
Which Shortcut Solution Do Question Answering Models Prefer to Learn?
K Shinoda, S Sugawara, A Aizawa
arXiv preprint arXiv:2211.16220, 2022
2022
Debiasing Masks: A New Framework for Shortcut Mitigation in NLU
JM Meissner, S Sugawara, A Aizawa
arXiv preprint arXiv:2210.16079, 2022
2022
Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering
K Shinoda, S Sugawara, A Aizawa
arXiv preprint arXiv:2210.14541, 2022
2022
How Well Do Multi-hop Reading Comprehension Models Understand Date Information?
X Ho, S Sugawara, A Aizawa
arXiv preprint arXiv:2210.05208, 2022
2022
Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios
M Ashida, S Sugawara
arXiv preprint arXiv:2209.07760, 2022
2022
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Articles 1–20