Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling
This addresses the need for explainable multi-hop question generation in interactive AI, offering a method that avoids costly intermediate labeling, though it is incremental as it builds on existing rewriting approaches.
The paper tackles the problem of generating complex multi-hop questions without needing labeled intermediate questions, by introducing an end-to-end question rewriting model that sequentially increases question complexity. The model effectively generates 3- and 4-hop questions paired with answers and benefits question answering training.
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.