Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question. Previous approaches usually only calculate question-aware passage representation and ignore passage-aware question representation when modeling the relationship between passage and question, which obviously cannot take the best of information between passage and question. In this work, we propose dual co-matching network (DCMN) which models the relationship among passage, question and answer options bidirectionally. Besides, inspired by how human solve multi-choice questions, we integrate two reading strategies into our model: (i) passage sentence selection that finds the most salient supporting sentences to answer the question, (ii) answer option interaction that encodes the comparison information between answer options. DCMN integrated with the two strategies (DCMN+) obtains state-of-the-art results on five multi-choice reading comprehension datasets which are from different domains: RACE, SemEval-2018 Task 11, ROCStories, COIN, MCTest.

本研究提出了双向共匹配网络（DCMN），它在模型化阅读理解中考虑了文章、问题和回答选项之间的关系，并将两种阅读策略融入模型中——段落句子选择与回答选项交互。 DCMN+ 取得了五个不同领域的阅读理解数据集的最优结果。

DCMN+: 多项选择阅读理解的双重共现网络