  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.
