FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation

• URL: http://arxiv.org/abs/2309.05007v2
• Date: Tue, 19 Sep 2023 07:51:03 GMT
• Title: FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation
• Authors: Yan Meng, Liangming Pan, Yixin Cao, Min-Yen Kan
• Abstract summary: We introduce the task of real-world information-seeking follow-up question generation (FQG) We construct FOLLOWUPQG, a dataset of over 3K real-world (initial question, answer, follow-up question)s collected from a forum layman providing Reddit-friendly explanations for open-ended questions. In contrast to existing datasets, questions in FOLLOWUPQG use more diverse pragmatic strategies to seek information, and they also show higher-order cognitive skills.
• Score: 38.78216651059955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of real-world information-seeking follow-up question generation (FQG), which aims to generate follow-up questions seeking a more in-depth understanding of an initial question and answer. We construct FOLLOWUPQG, a dataset of over 3K real-world (initial question, answer, follow-up question) tuples collected from a Reddit forum providing layman-friendly explanations for open-ended questions. In contrast to existing datasets, questions in FOLLOWUPQG use more diverse pragmatic strategies to seek information, and they also show higher-order cognitive skills (such as applying and relating). We evaluate current question generation models on their efficacy for generating follow-up questions, exploring how to generate specific types of follow-up questions based on step-by-step demonstrations. Our results validate FOLLOWUPQG as a challenging benchmark, as model-generated questions are adequate but far from human-raised questions in terms of informativeness and complexity.

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