Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension

TL;DR

This paper introduces a holistic graph network that models hierarchical logical relations at discourse and word levels to enhance machine reading comprehension, especially for complex logical reasoning tasks.

Contribution

It proposes a novel hierarchical interaction mechanism within a graph network to better capture multi-level logical relations in MRC.

Findings

Improves reasoning accuracy on ReClor and LogiQA datasets.

Demonstrates strong generalization on SNLI and ANLI datasets.

Provides in-depth analysis of logical relation understanding.

Abstract

Machine reading comprehension (MRC) poses new challenges over logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them. Due to the complexity of logic, logical relations exist at different granularity levels. However, most existing methods of logical reasoning individually focus on either entity-aware or discourse-based information but ignore the hierarchical relations that may even have mutual effects. In this paper, we propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning, to provide a more fine-grained relation extraction. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism to improve the interpretation of MRC systems. Experimental results on logical reasoning QA datasets (ReClor and LogiQA) and natural language inference datasets (SNLI and ANLI) show the effectiveness and generalization of our method, and in-depth analysis verifies its capability to understand complex logical relations.