Conversational question–answer generation is a task that automatically generates a largescale conversational question answering dataset based on input passages. In this paper, we introduce a novel framework that extracts questionworthy 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 signifcant improvement in the quality of synthetic data. Moreover, we prove that our framework can be effectively utilized for domain adaptation of conversational question answering.