  In response to the increasing use of interactive artificial intelligence, the
demand for the capacity to handle complex questions has increased. Multi-hop
question generation aims to generate complex questions that requires multi-step
reasoning over several documents. Previous studies have predominantly utilized
end-to-end models, wherein questions are decoded based on the representation of
context documents. However, these approaches lack the ability to explain the
reasoning process behind the generated multi-hop questions. Additionally, the
question rewriting approach, which incrementally increases the question
complexity, also has limitations due to the requirement of labeling data for
intermediate-stage questions. In this paper, we introduce an end-to-end
question rewriting model that increases question complexity through sequential
rewriting. The proposed model has the advantage of training with only the final
multi-hop questions, without intermediate questions. Experimental results
demonstrate the effectiveness of our model in generating complex questions,
particularly 3- and 4-hop questions, which are appropriately paired with input
answers. We also prove that our model logically and incrementally increases the
complexity of questions, and the generated multi-hop questions are also
beneficial for training question answering models.
