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        <title>Can Question Rewriting Help Conversational Question Answering?</title>
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            <title>Proceedings of the Third Workshop on Insights from Negative Results in NLP</title>
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            <namePart type="given">Shabnam</namePart>
            <namePart type="family">Tafreshi</namePart>
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            <namePart type="given">Arjun</namePart>
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    <abstract>Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA.We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.</abstract>
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    <identifier type="doi">10.18653/v1/2022.insights-1.13</identifier>
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%0 Conference Proceedings
%T Can Question Rewriting Help Conversational Question Answering?
%A Ishii, Etsuko
%A Xu, Yan
%A Cahyawijaya, Samuel
%A Wilie, Bryan
%Y Tafreshi, Shabnam
%Y Sedoc, João
%Y Rogers, Anna
%Y Drozd, Aleksandr
%Y Rumshisky, Anna
%Y Akula, Arjun
%S Proceedings of the Third Workshop on Insights from Negative Results in NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F ishii-etal-2022-question
%X Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form. Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA. To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA.We find, however, that the RL method is on par with the end-to-end baseline. We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.
%R 10.18653/v1/2022.insights-1.13
%U https://aclanthology.org/2022.insights-1.13
%U https://doi.org/10.18653/v1/2022.insights-1.13
%P 94-99

Markdown (Informal)

[Can Question Rewriting Help Conversational Question Answering?](https://aclanthology.org/2022.insights-1.13) (Ishii et al., insights 2022)

• Can Question Rewriting Help Conversational Question Answering? (Ishii et al., insights 2022)

ACL

• Etsuko Ishii, Yan Xu, Samuel Cahyawijaya, and Bryan Wilie. 2022. Can Question Rewriting Help Conversational Question Answering?. In Proceedings of the Third Workshop on Insights from Negative Results in NLP, pages 94–99, Dublin, Ireland. Association for Computational Linguistics.