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

TL;DR

This paper introduces DCMN+, a dual co-matching network that models passage, question, and answer relationships bidirectionally, incorporating human-inspired reading strategies to achieve state-of-the-art results in multi-choice reading comprehension.

Contribution

The paper proposes a novel dual co-matching network with integrated reading strategies for improved multi-choice reading comprehension performance.

Findings

Achieved state-of-the-art results on five diverse datasets.

Effectively models passage-question-answer relationships bidirectionally.

Incorporates human-like reading strategies to enhance comprehension.

Abstract

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.