  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.
