Open-Domain Conversational Question Answering with Historical Answers

2022/11/17 by Fang, Hung-Chieh, Hung, Kuo-Han, Huang, Chao-Wei +1 · 2 citations
#Computation and Language (cs.CL) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2211.09401

Abstract

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.

Cited by

• Adaptive Personalized Conversational Information Retrieval
• ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval

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