A Co-Matching Model for Multi-choice Reading Comprehension

Shuohang Wang, Mo Yu, Jing Jiang, Shiyu Chang


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P18-2118.pdf
Presentation:
 P18-2118.Presentation.pdf
Video:
 https://aclanthology.org/P18-2118.mp4
Code
 shuohangwang/comatch
Data
RACE