Dual Co-Matching Network for Multi-choice Reading Comprehension

27 Jan 2019  ·  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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Question Answering RACE DCMN_large RACE-m 73.4 # 3
RACE-h 68.1 # 2
RACE 69.7 # 3


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