DCMN+: Dual Co-Matching Network for Multi-choice Reading Comprehension

Understand

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.