Explainable Multi-hop Question Generation: An End-to-End Approach
  without Intermediate Question Labeling
Seonjeong Hwang Yunsu Kim Gary Geunbae Lee
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.