Conversational QA Dataset Generation with Answer Revision

Seonjeong Hwang, Gary Geunbae Lee

Keywords: question generation, conversational question answering, conversation question-answer generation, question-answer generation

TL;DR: We introduce a conversation question-answer generation system that improves the quality of answers by revising them in a conversational question generation module.

Abstract: 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.