论文标题
带有历史答案的开放域对话问题回答
Open-Domain Conversational Question Answering with Historical Answers
论文作者
论文摘要
开放域的对话问题回答可以看作是两个任务：段落检索和会话问题回答，前者依靠从大型语料库中选择候选段落，后者需要更好地理解具有上下文的问题以预测答案。本文提出，Curedr-QA利用历史答案来提高检索性能并进一步取得更好的回答性能。在我们提出的框架中，猎犬使用教师学生框架来减少上一轮的噪音。我们在基准数据集上进行的实验表明，我们的模型在提取和生成读取器设置中的现有基准都优于现有基准，这很好地证明了历史答案的有效性是开放域的对话对话问题的回答。
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.
