Open-domain conversational question answering can be viewed as two tasks:
passage retrieval and conversational question answering, where the former
relies on selecting candidate passages from a large corpus and the latter
requires better understanding of a question with contexts to predict the
answers. This paper proposes ConvADR-QA that leverages historical answers to
boost retrieval performance and further achieves better answering performance.
In our proposed framework, the retrievers use a teacher-student framework to
reduce noises from previous turns. Our experiments on the benchmark dataset,
OR-QuAC, demonstrate that our model outperforms existing baselines in both
extractive and generative reader settings, well justifying the effectiveness of
historical answers for open-domain conversational question answering.

本文提出了 ConvADR-QA，该方法在 Open-domain conversational question answering 中使用历史答案来提高文本检索和回答效率。通过引入 teacher-student 框架来减少上一轮的噪声，实验结果表明我们的模型在提取式和生成式读者设置方面均优于现有的基线模型，充分证明了历史答案在开放领域会话问答中的有效性。