A Co-Matching Model for Multi-choice Reading Comprehension
About
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.
Shuohang Wang, Mo Yu, Shiyu Chang, Jing Jiang• 2018
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Machine Reading Comprehension | RACE (test) | RACE Accuracy (Medium)55.8 | 111 | |
| Machine Reading Comprehension | RACE | RACE Overall Accuracy50.4 | 38 | |
| Multiple-choice reading comprehension | ViMMRC 2.0 | Accuracy41.21 | 21 | |
| Multiple-choice reading comprehension | ViRCSoSciD | Accuracy34.76 | 12 |
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