Exploiting Intrinsic Duality for Multi-Hop Question Generation

2026-08-01

AI Summary

This work addresses the limitation of existing approaches in multi-hop question generation, which overlook the intrinsic duality between question generation and question answering, thereby constraining generation quality. To overcome this, the paper proposes the QQ framework, which explicitly models this dual relationship for the first time by jointly training multi-hop question generation and question answering within a unified architecture. The framework incorporates bidirectional alignment constraints and a contrastive learning mechanism to strengthen semantic correspondence between generated questions and their answers. Experimental results demonstrate that the proposed method significantly improves question quality on the HotpotQA and MuSiQue datasets, with both automatic metrics and human evaluations confirming its superiority over baseline approaches.

Abstract

Multi hop question generation (MQG) aims to generate questions from multiple given documents and target answers, whereas question answering (QA) focuses on deriving answers from documents given specific questions. Although MQG and QA are inherently dual tasks, most existing MQG studies largely overlook this intrinsic duality. To address this limitation, we propose QQ, a novel framework that exploits the duality between Question and answer for multi hop Question generation. Specifically, QQ employs a unified architecture functioning simultaneously as both an MQG and a QA model to fully leverage their interdependence. Our framework is driven by two key mechanisms: (i) enforcing bidirectional alignment constraints to ensure strict mutual correspondence between the questions generated by the MQG model and the answers produced by the QA model; and (ii) applying contrastive learning to pull paired question answer representations closer while pushing unpaired ones apart, thereby reinforcing this correspondence. Extensive automatic and human evaluations on the HotpotQA and MuSiQue datasets demonstrate that the QQ framework significantly improves the quality of generated multi hop questions.

Problem

• multi-hop question generation
• question answering
• intrinsic duality
• question-answer correspondence
• natural language generation

Innovation

• intrinsic duality
• multi-hop question generation
• unified architecture
• bidirectional alignment
• contrastive learning