A Co-Matching Model for Multi-choice Reading Comprehension

Shuohang Wang, Mo Yu, Jing Jiang, Shiyu Chang

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.