Kuo-Han Hung


2022

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Open-Domain Conversational Question Answering with Historical Answers
Hung-Chieh Fang | Kuo-Han Hung | Chen-Wei Huang | Yun-Nung Chen
Findings of the Association for Computational Linguistics: AACL-IJCNLP 2022

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. 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.