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        <title>Interactive Question Clarification in Dialogue via Reinforcement Learning</title>
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        <namePart type="given">Xiang</namePart>
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    <name type="personal">
        <namePart type="given">Xiaolong</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics: Industry Track</title>
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    <abstract>Coping with ambiguous questions has been a perennial problem in real-world dialogue systems. Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user. In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents. We list the chosen labels as intent phrases to the user for further confirmation. The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified. The model is trained using reinforcement learning with a deep policy network. We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-industry.8</identifier>
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%0 Conference Proceedings
%T Interactive Question Clarification in Dialogue via Reinforcement Learning
%A Hu, Xiang
%A Wen, Zujie
%A Wang, Yafang
%A Li, Xiaolong
%A de Melo, Gerard
%Y Clifton, Ann
%Y Napoles, Courtney
%S Proceedings of the 28th International Conference on Computational Linguistics: Industry Track
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Online
%F hu-etal-2020-interactive
%X Coping with ambiguous questions has been a perennial problem in real-world dialogue systems. Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user. In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents. We list the chosen labels as intent phrases to the user for further confirmation. The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified. The model is trained using reinforcement learning with a deep policy network. We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.
%R 10.18653/v1/2020.coling-industry.8
%U https://aclanthology.org/2020.coling-industry.8
%U https://doi.org/10.18653/v1/2020.coling-industry.8
%P 78-89

Markdown (Informal)

[Interactive Question Clarification in Dialogue via Reinforcement Learning](https://aclanthology.org/2020.coling-industry.8) (Hu et al., COLING 2020)

• Interactive Question Clarification in Dialogue via Reinforcement Learning (Hu et al., COLING 2020)

ACL

• Xiang Hu, Zujie Wen, Yafang Wang, Xiaolong Li, and Gerard de Melo. 2020. Interactive Question Clarification in Dialogue via Reinforcement Learning. In Proceedings of the 28th International Conference on Computational Linguistics: Industry Track, pages 78–89, Online. International Committee on Computational Linguistics.