论文标题
对话质量质量检查数据集生成带答案修订版
Conversational QA Dataset Generation with Answer Revision
论文作者
论文摘要
对话问题 - 转答是一项任务，它会根据输入段落自动生成一个大规模的对话问题回答数据集。在本文中，我们介绍了一个新颖的框架，该框架从段落中提取了值得一提的短语，然后在考虑以前的对话时产生相应的问题。特别是，我们的框架在产生问题后修改了提取的答案，以便答案与配对的问题完全匹配。实验结果表明，我们简单的答案修订方法可显着改善合成数据的质量。此外，我们证明我们的框架可以有效地用于域的适应会话问题回答。
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.
