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2020 – today
- 2022
[c14]Saku Sugawara, Nikita Nangia, Alex Warstadt, Samuel R. Bowman:
What Makes Reading Comprehension Questions Difficult? ACL (1) 2022: 6951-6971- [c13]Mana Ashida, Saku Sugawara:
Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios. COLING 2022: 3606-3630 - [c12]Xanh Ho, Saku Sugawara, Akiko Aizawa:
How Well Do Multi-hop Reading Comprehension Models Understand Date Information? AACL/IJCNLP (2) 2022: 470-479 - [c11]Teruhito Kanazawa, Hidekazu Nakawatase, Saku Sugawara, Chifumi Nishioka:
Interdisciplinarity Inference Using a Cross Encoder between Research Projects. SCIS/ISIS 2022: 1-4 - [i16]Saku Sugawara, Nikita Nangia, Alex Warstadt, Samuel R. Bowman:
What Makes Reading Comprehension Questions Difficult? CoRR abs/2203.06342 (2022) - [i15]Xanh Ho, Johannes Mario Meissner, Saku Sugawara, Akiko Aizawa:
A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension. CoRR abs/2209.01824 (2022) - [i14]Mana Ashida, Saku Sugawara:
Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios. CoRR abs/2209.07760 (2022) - [i13]Xanh Ho, Saku Sugawara, Akiko Aizawa:
How Well Do Multi-hop Reading Comprehension Models Understand Date Information? CoRR abs/2210.05208 (2022) - [i12]Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa:
Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering. CoRR abs/2210.14541 (2022) - [i11]Johannes Mario Meissner, Saku Sugawara, Akiko N. Aizawa:
Debiasing Masks: A New Framework for Shortcut Mitigation in NLU. CoRR abs/2210.16079 (2022) - [i10]Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa:
Penalizing Confident Predictions on Largely Perturbed Inputs Does Not Improve Out-of-Distribution Generalization in Question Answering. CoRR abs/2211.16093 (2022) - [i9]Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa:
Which Shortcut Solution Do Question Answering Models Prefer to Learn? CoRR abs/2211.16220 (2022) - [i8]Hongkuan Zhang, Saku Sugawara, Akiko Aizawa, Lei Zhou, Ryohei Sasano, Koichi Takeda:
Cross-Modal Similarity-Based Curriculum Learning for Image Captioning. CoRR abs/2212.07075 (2022) - 2021
- [c10]Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa:
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation. ACL (student) 2021: 197-214 - [c9]Johannes Mario Meissner, Napat Thumwanit, Saku Sugawara, Akiko Aizawa:
Embracing Ambiguity: Shifting the Training Target of NLI Models. ACL/IJCNLP (2) 2021: 862-869 - [c8]Nikita Nangia, Saku Sugawara, Harsh Trivedi, Alex Warstadt, Clara Vania, Samuel R. Bowman:
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? ACL/IJCNLP (1) 2021: 1221-1235 - [c7]Saku Sugawara, Pontus Stenetorp, Akiko Aizawa:
Benchmarking Machine Reading Comprehension: A Psychological Perspective. EACL 2021: 1592-1612 - [i7]Nikita Nangia, Saku Sugawara, Harsh Trivedi, Alex Warstadt, Clara Vania, Samuel R. Bowman:
What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks? CoRR abs/2106.00794 (2021) - [i6]Johannes Mario Meissner, Napat Thumwanit, Saku Sugawara, Akiko Aizawa:
Embracing Ambiguity: Shifting the Training Target of NLI Models. CoRR abs/2106.03020 (2021) - [i5]Kazutoshi Shinoda, Saku Sugawara, Akiko Aizawa:
Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlap. CoRR abs/2109.11256 (2021) - 2020
- [c6]Saku Sugawara, Pontus Stenetorp, Kentaro Inui, Akiko Aizawa:
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets. AAAI 2020: 8918-8927 - [c5]Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa:
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps. COLING 2020: 6609-6625 - [i4]Saku Sugawara, Pontus Stenetorp, Akiko Aizawa:
Prerequisites for Explainable Machine Reading Comprehension: A Position Paper. CoRR abs/2004.01912 (2020) - [i3]Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, Akiko Aizawa:
Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps. CoRR abs/2011.01060 (2020)
2010 – 2019
- 2019
- [i2]Saku Sugawara, Pontus Stenetorp, Kentaro Inui, Akiko Aizawa:
Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets. CoRR abs/1911.09241 (2019) - 2018
- [c4]Saku Sugawara, Kentaro Inui, Satoshi Sekine, Akiko Aizawa:
What Makes Reading Comprehension Questions Easier? EMNLP 2018: 4208-4219 - [i1]Saku Sugawara, Kentaro Inui, Satoshi Sekine, Akiko Aizawa:
What Makes Reading Comprehension Questions Easier? CoRR abs/1808.09384 (2018) - 2017
- [c3]Saku Sugawara, Hikaru Yokono, Akiko Aizawa:
Prerequisite Skills for Reading Comprehension: Multi-Perspective Analysis of MCTest Datasets and Systems. AAAI 2017: 3089-3096 - [c2]Saku Sugawara, Yusuke Kido, Hikaru Yokono, Akiko Aizawa:
Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability. ACL (1) 2017: 806-817 - 2016
- [c1]Kimi Kaneko, Saku Sugawara, Koji Mineshima, Daisuke Bekki:
Annotation and Analysis of Discourse Relations, Temporal Relations and Multi-Layered Situational Relations in Japanese Texts. ALR@COLING 2016: 10-19
Coauthor Index
aka: Akiko N. Aizawa
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last updated on 2023-01-11 21:19 CET by the dblp team
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