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

Understand

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.