DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension
This work addresses the challenge of improving accuracy in multi-choice reading comprehension tasks across various domains, representing an incremental advancement with novel method integration.
The authors tackled the problem of multi-choice reading comprehension by proposing a dual co-matching network that models bidirectional relationships among passage, question, and answer options, achieving state-of-the-art results on five diverse datasets.
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.