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.

中文翻译：

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DCMN+：用于多选阅读理解的双协同匹配网络

多项选择阅读理解是一项具有挑战性的任务，要在给定段落和问题时从一组候选选项中选择答案。以前的方法在对文章和问题之间的关系进行建模时通常只计算问题感知的文章表示，而忽略了文章和问题的关系，这显然不能充分利用文章和问题之间的信息。在这项工作中，我们提出了双重协同匹配网络（DCMN），它双向建模通道、问题和答案选项之间的关系。此外，受人类如何解决多项选择题的启发，我们将两种阅读策略整合到我们的模型中：（i）段落选择，找到最显着的支持句子来回答问题，(ii) 对答案选项之间的比较信息进行编码的答案选项交互。与两种策略相结合的 DCMN (DCMN+) 在来自不同领域的五个多选阅读理解数据集上获得了最先进的结果：RACE、SemEval-2018 Task 11、ROCStories、COIN、MCTest。