Abstract
Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer. Experimental results on the RACE dataset demonstrate that our approach achieves state-of-the-art performance.- Anthology ID:
- P18-2118
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 746–751
- Language:
- URL:
- https://aclanthology.org/P18-2118
- DOI:
- 10.18653/v1/P18-2118
- Cite (ACL):
- Shuohang Wang, Mo Yu, Jing Jiang, and Shiyu Chang. 2018. A Co-Matching Model for Multi-choice Reading Comprehension. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 746–751, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- A Co-Matching Model for Multi-choice Reading Comprehension (Wang et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/P18-2118.pdf
- Code
- shuohangwang/comatch
- Data
- RACE