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.
翻译:开放式对话问答可被视为两项任务:通过检索和对话回答,前者依赖于从大体中选择候选段落,后者要求更好地了解问题和预测答案的背景。本文件建议ConvADR-QA利用历史答案来提高检索性能并进一步实现更好的回答性能。在我们提议的框架中,检索者使用教师-学生框架来减少以前转弯的噪音。我们在OR-QuAC基准数据集的实验显示,我们的模型在采掘和基因化阅读器环境中都比现有基线高,这很好地证明历史答案对开放面对话问题回答的有效性。