# QA4QG: Using Question Answering to Constrain Multi-Hop Question Generation

arXiv: https://arxiv.org/abs/2202.06538 · HTML: https://mlsanity.com/paper/2202.06538/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 17 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 12 of 12)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Hierarchical Graph Network for Multi-hop Question Answering](https://mlsanity.com/paper/1911.03631/) (2019)
- [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://mlsanity.com/paper/1909.06356/) (2019)
- [Semantic Graphs for Generating Deep Questions](https://mlsanity.com/paper/2004.12704/) (2020)
- [Question Answering and Question Generation as Dual Tasks](https://mlsanity.com/paper/1706.02027/) (2017)
- [Improving Neural Question Generation using Answer Separation](https://mlsanity.com/paper/1809.02393/) (2018)
- [A Simple Yet Strong Pipeline for HotpotQA](https://mlsanity.com/paper/2004.06753/) (2020)
- [Multi-hop Question Generation with Graph Convolutional Network](https://mlsanity.com/paper/2010.09240/) (2020)
- [Exploring Question-Specific Rewards for Generating Deep Questions](https://mlsanity.com/paper/2011.01102/) (2020)
- [Stronger Transformers for Neural Multi-Hop Question Generation](https://mlsanity.com/paper/2010.11374/) (2020)
- [Reinforced Multi-task Approach for Multi-hop Question Generation](https://mlsanity.com/paper/2004.02143/) (2020)

## Built on by (most cited first, 3 of 3)

- [MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback](https://mlsanity.com/paper/2410.13191/) (2024)
- [Multi-Hop Question Generation via Dual-Perspective Keyword Guidance](https://mlsanity.com/paper/2505.15299/) (2025)
- [Exploiting Intrinsic Duality for Multi-Hop Question Generation](https://mlsanity.com/paper/2608.00712/) (2026)

## Abstract (arXiv)

Multi-hop question generation (MQG) aims to generate complex questions which require reasoning over multiple pieces of information of the input passage. Most existing work on MQG has focused on exploring graph-based networks to equip the traditional Sequence-to-sequence framework with reasoning ability. However, these models do not take full advantage of the constraint between questions and answers. Furthermore, studies on multi-hop question answering (QA) suggest that Transformers can replace the graph structure for multi-hop reasoning. Therefore, in this work, we propose a novel framework, QA4QG, a QA-augmented BART-based framework for MQG. It augments the standard BART model with an additional multi-hop QA module to further constrain the generated question. Our results on the HotpotQA dataset show that QA4QG outperforms all state-of-the-art models, with an increase of 8 BLEU-4 and 8 ROUGE points compared to the best results previously reported. Our work suggests the advantage of introducing pre-trained language models and QA module for the MQG task.