{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/explainable-multi-hop-question-generation-an","title":"Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling","arxiv_id":"2404.00571","date":"2024-03-31","proceeding":null,"authors":["Seonjeong Hwang","Yunsu Kim","Gary Geunbae Lee"],"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.","url_abs":"https://arxiv.org/abs/2404.00571v1","url_pdf":"https://arxiv.org/pdf/2404.00571v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"explainable-multi-hop-question-generation-an","repo_url":"https://github.com/seonjeonghwang/e2eqr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"question-generation","task_name":"Question Generation"},{"task_slug":"question-rewriting","task_name":"Question Rewriting"},{"task_slug":"question-generation","task_name":"Question-Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.00571","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}