@inproceedings{chen-etal-2022-reinforced,
title = "Reinforced Question Rewriting for Conversational Question Answering",
author = "Chen, Zhiyu and
Zhao, Jie and
Fang, Anjie and
Fetahu, Besnik and
Rokhlenko, Oleg and
Malmasi, Shervin",
editor = "Li, Yunyao and
Lazaridou, Angeliki",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-industry.36",
doi = "10.18653/v1/2022.emnlp-industry.36",
pages = "357--370",
abstract = "Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conversational questions into self-contained ones is an emerging solution in industry settings as it allows using existing single-turn QA systems to avoid training a CQA model from scratch. Previous work trains rewriting models using human rewrites as supervision. However, such objectives are disconnected with QA models and therefore more human-like rewrites do not guarantee better QA performance. In this paper we propose using QA feedback to supervise the rewriting model with reinforcement learning. Experiments show that our approach can effectively improve QA performance over baselines for both extractive and retrieval QA. Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.",
}
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<abstract>Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conversational questions into self-contained ones is an emerging solution in industry settings as it allows using existing single-turn QA systems to avoid training a CQA model from scratch. Previous work trains rewriting models using human rewrites as supervision. However, such objectives are disconnected with QA models and therefore more human-like rewrites do not guarantee better QA performance. In this paper we propose using QA feedback to supervise the rewriting model with reinforcement learning. Experiments show that our approach can effectively improve QA performance over baselines for both extractive and retrieval QA. Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.</abstract>
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%0 Conference Proceedings
%T Reinforced Question Rewriting for Conversational Question Answering
%A Chen, Zhiyu
%A Zhao, Jie
%A Fang, Anjie
%A Fetahu, Besnik
%A Rokhlenko, Oleg
%A Malmasi, Shervin
%Y Li, Yunyao
%Y Lazaridou, Angeliki
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F chen-etal-2022-reinforced
%X Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conversational questions into self-contained ones is an emerging solution in industry settings as it allows using existing single-turn QA systems to avoid training a CQA model from scratch. Previous work trains rewriting models using human rewrites as supervision. However, such objectives are disconnected with QA models and therefore more human-like rewrites do not guarantee better QA performance. In this paper we propose using QA feedback to supervise the rewriting model with reinforcement learning. Experiments show that our approach can effectively improve QA performance over baselines for both extractive and retrieval QA. Furthermore, human evaluation shows that our method can generate more accurate and detailed rewrites when compared to human annotations.
%R 10.18653/v1/2022.emnlp-industry.36
%U https://aclanthology.org/2022.emnlp-industry.36
%U https://doi.org/10.18653/v1/2022.emnlp-industry.36
%P 357-370
Markdown (Informal)
[Reinforced Question Rewriting for Conversational Question Answering](https://aclanthology.org/2022.emnlp-industry.36) (Chen et al., EMNLP 2022)
ACL
- Zhiyu Chen, Jie Zhao, Anjie Fang, Besnik Fetahu, Oleg Rokhlenko, and Shervin Malmasi. 2022. Reinforced Question Rewriting for Conversational Question Answering. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 357–370, Abu Dhabi, UAE. Association for Computational Linguistics.