Open-Domain Conversational Question Answering with Historical Answers
- URL: http://arxiv.org/abs/2211.09401v1
- Date: Thu, 17 Nov 2022 08:20:57 GMT
- Title: Open-Domain Conversational Question Answering with Historical Answers
- Authors: Hung-Chieh Fang, Kuo-Han Hung, Chao-Wei Huang, Yun-Nung Chen
- Abstract summary: This paper proposes ConvADR-QA that leverages historical answers to boost retrieval 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.
- Score: 29.756094955426597
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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