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    <titleInfo>
        <title>Learning to Identify Follow-Up Questions in Conversational Question Answering</title>
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    <name type="personal">
        <namePart type="given">Souvik</namePart>
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    <name type="personal">
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions. Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up question of the current conversation, for more effective answer finding subsequently. In this paper, we introduce a new follow-up question identification task. We propose a three-way attentive pooling network that determines the suitability of a follow-up question by capturing pair-wise interactions between the associated passage, the conversation history, and a candidate follow-up question. It enables the model to capture topic continuity and topic shift while scoring a particular candidate follow-up question. Experiments show that our proposed three-way attentive pooling network outperforms all baseline systems by significant margins.</abstract>
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    <identifier type="doi">10.18653/v1/2020.acl-main.90</identifier>
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        <date>2020-07</date>
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            <start>959</start>
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%0 Conference Proceedings
%T Learning to Identify Follow-Up Questions in Conversational Question Answering
%A Kundu, Souvik
%A Lin, Qian
%A Ng, Hwee Tou
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F kundu-etal-2020-learning
%X Despite recent progress in conversational question answering, most prior work does not focus on follow-up questions. Practical conversational question answering systems often receive follow-up questions in an ongoing conversation, and it is crucial for a system to be able to determine whether a question is a follow-up question of the current conversation, for more effective answer finding subsequently. In this paper, we introduce a new follow-up question identification task. We propose a three-way attentive pooling network that determines the suitability of a follow-up question by capturing pair-wise interactions between the associated passage, the conversation history, and a candidate follow-up question. It enables the model to capture topic continuity and topic shift while scoring a particular candidate follow-up question. Experiments show that our proposed three-way attentive pooling network outperforms all baseline systems by significant margins.
%R 10.18653/v1/2020.acl-main.90
%U https://aclanthology.org/2020.acl-main.90
%U https://doi.org/10.18653/v1/2020.acl-main.90
%P 959-968

Markdown (Informal)

[Learning to Identify Follow-Up Questions in Conversational Question Answering](https://aclanthology.org/2020.acl-main.90) (Kundu et al., ACL 2020)

• Learning to Identify Follow-Up Questions in Conversational Question Answering (Kundu et al., ACL 2020)

ACL

• Souvik Kundu, Qian Lin, and Hwee Tou Ng. 2020. Learning to Identify Follow-Up Questions in Conversational Question Answering. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 959–968, Online. Association for Computational Linguistics.