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        <title>Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment</title>
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        <namePart type="given">Siqi</namePart>
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    <abstract>In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other. For the sake of rational knowledge utilization and coherent conversation flow, a dialogue strategy which controls knowledge selection is instantiated and continuously adapted via reinforcement learning. Under the deployed strategy, knowledge grounded conversations are conducted with two dialogue agents. The generated dialogues are comprehensively evaluated on aspects like informativeness and coherence, which are aligned with our objective and human instinct. These assessments are integrated as a compound reward to guide the evolution of dialogue strategy via policy gradient. Comprehensive experiments have been carried out on the publicly available dataset, demonstrating that the proposed method outperforms the other state-of-the-art approaches significantly.</abstract>
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%0 Conference Proceedings
%T Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment
%A Bao, Siqi
%A He, Huang
%A Wang, Fan
%A Lian, Rongzhong
%A Wu, Hua
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F bao-etal-2019-know
%X In this paper, a novel Generation-Evaluation framework is developed for multi-turn conversations with the objective of letting both participants know more about each other. For the sake of rational knowledge utilization and coherent conversation flow, a dialogue strategy which controls knowledge selection is instantiated and continuously adapted via reinforcement learning. Under the deployed strategy, knowledge grounded conversations are conducted with two dialogue agents. The generated dialogues are comprehensively evaluated on aspects like informativeness and coherence, which are aligned with our objective and human instinct. These assessments are integrated as a compound reward to guide the evolution of dialogue strategy via policy gradient. Comprehensive experiments have been carried out on the publicly available dataset, demonstrating that the proposed method outperforms the other state-of-the-art approaches significantly.
%R 10.18653/v1/P19-1535
%U https://aclanthology.org/P19-1535
%U https://doi.org/10.18653/v1/P19-1535
%P 5382-5391

Markdown (Informal)

[Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment](https://aclanthology.org/P19-1535) (Bao et al., ACL 2019)

• Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment (Bao et al., ACL 2019)

ACL

• Siqi Bao, Huang He, Fan Wang, Rongzhong Lian, and Hua Wu. 2019. Know More about Each Other: Evolving Dialogue Strategy via Compound Assessment. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5382–5391, Florence, Italy. Association for Computational Linguistics.