Dual Co-Matching Network for Multi-choice Reading Comprehension

TL;DR

This paper introduces the Dual Co-Matching Network (DCMN), a novel model for multi-choice reading comprehension that models passage, question, and answer relationships bidirectionally, achieving state-of-the-art results on the RACE dataset.

Contribution

The paper proposes DCMN, which simultaneously models passage-aware question and answer representations, improving upon existing methods that consider only one aspect.

Findings

Achieves new state-of-the-art on RACE dataset

Demonstrates effectiveness of bidirectional co-matching approach

Outperforms previous models in multi-choice reading comprehension

Abstract

Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose \textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Experimental result show that our proposed model achieves new state-of-the-art results.