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

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