# Open-Domain Conversational Question Answering with Historical Answers

Source: [Open-Domain Conversational Question Answering with Historical Answers](<https://www.sota2.com/research/paper/68fa6e001e209131cea3d520>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:48:10.793Z

Authors: Hung-Chieh Fang, Kuo-Han Hung, Chao-Wei Huang, Yun-Nung Chen

Published: 2022-11-17T08:20:57.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.09401>)

[PDF](<https://arxiv.org/pdf/2211.09401.pdf>)