  Conversational question--answer generation is a task that automatically
generates a large-scale conversational question answering dataset based on
input passages. In this paper, we introduce a novel framework that extracts
question-worthy phrases from a passage and then generates corresponding
questions considering previous conversations. In particular, our framework
revises the extracted answers after generating questions so that answers
exactly match paired questions. Experimental results show that our simple
answer revision approach leads to significant improvement in the quality of
synthetic data. Moreover, we prove that our framework can be effectively
utilized for domain adaptation of conversational question answering.
