What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys

• URL: http://arxiv.org/abs/2205.10977v2
• Date: Fri, 13 Oct 2023 15:38:46 GMT
• Title: What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveys
• Authors: Yubin Ge, Ziang Xiao, Jana Diesner, Heng Ji, Karrie Karahalios, Hari Sundaram
• Abstract summary: We propose a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge. We then propose a two-staged knowledge-driven model for the task, which generates informative and coherent follow-up questions.
• Score: 63.51903260461746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating follow-up questions on the fly could significantly improve conversational survey quality and user experiences by enabling a more dynamic and personalized survey structure. In this paper, we proposed a novel task for knowledge-driven follow-up question generation in conversational surveys. We constructed a new human-annotated dataset of human-written follow-up questions with dialogue history and labeled knowledge in the context of conversational surveys. Along with the dataset, we designed and validated a set of reference-free Gricean-inspired evaluation metrics to systematically evaluate the quality of generated follow-up questions. We then propose a two-staged knowledge-driven model for the task, which generates informative and coherent follow-up questions by using knowledge to steer the generation process. The experiments demonstrate that compared to GPT-based baseline models, our two-staged model generates more informative, coherent, and clear follow-up questions.

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