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    <abstract>Typical generative dialogue models utilize the dialogue history to generate the response. However, since one dialogue utterance can often be appropriately answered by multiple distinct responses, generating a desired response solely based on the historical information is not easy. Intuitively, if the chatbot can foresee in advance what the user would talk about (i.e., the dialogue future) after receiving its response, it could possibly provide a more informative response. Accordingly, we propose a novel dialogue generation framework named ProphetChat that utilizes the simulated dialogue futures in the inference phase to enhance response generation. To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector. With the simulated futures, we then utilize the ensemble of a history-to-response generator and a future-to-response generator to jointly generate a more informative response. Experiments on two popular open-domain dialogue datasets demonstrate that ProphetChat can generate better responses over strong baselines, which validates the advantages of incorporating the simulated dialogue futures.</abstract>
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%0 Conference Proceedings
%T ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation
%A Liu, Chang
%A Tan, Xu
%A Tao, Chongyang
%A Fu, Zhenxin
%A Zhao, Dongyan
%A Liu, Tie-Yan
%A Yan, Rui
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F liu-etal-2022-prophetchat
%X Typical generative dialogue models utilize the dialogue history to generate the response. However, since one dialogue utterance can often be appropriately answered by multiple distinct responses, generating a desired response solely based on the historical information is not easy. Intuitively, if the chatbot can foresee in advance what the user would talk about (i.e., the dialogue future) after receiving its response, it could possibly provide a more informative response. Accordingly, we propose a novel dialogue generation framework named ProphetChat that utilizes the simulated dialogue futures in the inference phase to enhance response generation. To enable the chatbot to foresee the dialogue future, we design a beam-search-like roll-out strategy for dialogue future simulation using a typical dialogue generation model and a dialogue selector. With the simulated futures, we then utilize the ensemble of a history-to-response generator and a future-to-response generator to jointly generate a more informative response. Experiments on two popular open-domain dialogue datasets demonstrate that ProphetChat can generate better responses over strong baselines, which validates the advantages of incorporating the simulated dialogue futures.
%R 10.18653/v1/2022.acl-long.68
%U https://aclanthology.org/2022.acl-long.68
%U https://doi.org/10.18653/v1/2022.acl-long.68
%P 962-973

Markdown (Informal)

[ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation](https://aclanthology.org/2022.acl-long.68) (Liu et al., ACL 2022)

• ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation (Liu et al., ACL 2022)

ACL

• Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, and Rui Yan. 2022. ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 962–973, Dublin, Ireland. Association for Computational Linguistics.