Papers › Dual Co-Matching Network for Multi-choice Reading Comprehension
Dual Co-Matching Network for Multi-choice Reading Comprehension
Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose \textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Experimental result show that our proposed model achieves new state-of-the-art results.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | RACE | DCMN_large | RACE | 69.7 | #3 of 7 | Archive leaderboard | report |
| Question Answering | RACE | DCMN_large | RACE-h | 68.1 | #3 of 7 | Archive leaderboard | report |
| Question Answering | RACE | DCMN_large | RACE-m | 73.4 | #3 of 7 | Archive leaderboard | report |
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