Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling

• URL: http://arxiv.org/abs/2404.00571v1
• Date: Sun, 31 Mar 2024 06:03:54 GMT
• Title: Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling
• Authors: Seonjeong Hwang, Yunsu Kim, Gary Geunbae Lee,
• Abstract summary: 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. This paper introduces an end-to-end question rewriting model that increases question complexity through sequential rewriting.
• Score: 6.635572580071933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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