Open-Domain Conversational Question Answering with Historical Answers
This work addresses the problem of improving accuracy in conversational question answering for AI systems, representing an incremental advancement by integrating historical answers into retrieval and answering processes.
The paper tackled open-domain conversational question answering by leveraging historical answers to improve passage retrieval and answer prediction, achieving superior performance on the OR-QuAC benchmark in both extractive and generative settings.
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.