Ask to Understand: Question Generation for Multi-hop Question Answering

TL;DR

This paper introduces a question generation approach for multi-hop QA that enhances interpretability and performance by generating logical sub-questions, outperforming existing graph network and question decomposition methods.

Contribution

The paper proposes an end-to-end question generation module integrated with a QA system, improving interpretability and sub-question quality in multi-hop question answering.

Findings

QG module improves answer accuracy on HotpotQA

Generated sub-questions are more fluent, consistent, and diverse

Human evaluation confirms enhanced interpretability

Abstract

Multi-hop Question Answering (QA) requires the machine to answer complex questions by finding scattering clues and reasoning from multiple documents. Graph Network (GN) and Question Decomposition (QD) are two common approaches at present. The former uses the "black-box" reasoning process to capture the potential relationship between entities and sentences, thus achieving good performance. At the same time, the latter provides a clear reasoning logical route by decomposing multi-hop questions into simple single-hop sub-questions. In this paper, we propose a novel method to complete multi-hop QA from the perspective of Question Generation (QG). Specifically, we carefully design an end-to-end QG module on the basis of a classical QA module, which could help the model understand the context by asking inherently logical sub-questions, thus inheriting interpretability from the QD-based method and showing superior performance. Experiments on the HotpotQA dataset demonstrate that the effectiveness of our proposed QG module, human evaluation further clarifies its interpretability quantitatively, and thorough analysis shows that the QG module could generate better sub-questions than QD methods in terms of fluency, consistency, and diversity.