  Conversational question answering (CQA) facilitates an incremental and
interactive understanding of a given context, but building a CQA system is
difficult for many domains due to the problem of data scarcity. In this paper,
we introduce a novel method to synthesize data for CQA with various question
types, including open-ended, closed-ended, and unanswerable questions. We
design a different generation flow for each question type and effectively
combine them in a single, shared framework. Moreover, we devise a hierarchical
answerability classification (hierarchical AC) module that improves quality of
the synthetic data while acquiring unanswerable questions. Manual inspections
show that synthetic data generated with our framework have characteristics very
similar to those of human-generated conversations. Across four domains, CQA
systems trained on our synthetic data indeed show good performance close to the
systems trained on human-annotated data.
