@inproceedings{zhong-etal-2022-less,
title = "Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation",
author = "Zhong, Hanxun and
Dou, Zhicheng and
Zhu, Yutao and
Qian, Hongjin and
Wen, Ji-Rong",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.426/",
doi = "10.18653/v1/2022.naacl-main.426",
pages = "5808--5820",
abstract = "Personalized dialogue systems explore the problem of generating responses that are consistent with the user`s personality, which has raised much attention in recent years. Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation. Since the dialogue history is usually long and noisy, most existing methods truncate the dialogue history to model the user`s personality. Such methods can generate some personalized responses, but a large part of dialogue history is wasted, leading to sub-optimal performance of personalized response generation. In this work, we propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more abundant and accurate persona information. Specifically, we design an MSP model which consists of three personal information refiners and a personalized response generator. With these multi-level refiners, we can sparsely extract the most valuable information (tokens) from the dialogue history and leverage other similar users' data to enhance personalization. Experimental results on two real-world datasets demonstrate the superiority of our model in generating more informative and personalized responses."
}
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<abstract>Personalized dialogue systems explore the problem of generating responses that are consistent with the user‘s personality, which has raised much attention in recent years. Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation. Since the dialogue history is usually long and noisy, most existing methods truncate the dialogue history to model the user‘s personality. Such methods can generate some personalized responses, but a large part of dialogue history is wasted, leading to sub-optimal performance of personalized response generation. In this work, we propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more abundant and accurate persona information. Specifically, we design an MSP model which consists of three personal information refiners and a personalized response generator. With these multi-level refiners, we can sparsely extract the most valuable information (tokens) from the dialogue history and leverage other similar users’ data to enhance personalization. Experimental results on two real-world datasets demonstrate the superiority of our model in generating more informative and personalized responses.</abstract>
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%0 Conference Proceedings
%T Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation
%A Zhong, Hanxun
%A Dou, Zhicheng
%A Zhu, Yutao
%A Qian, Hongjin
%A Wen, Ji-Rong
%Y Carpuat, Marine
%Y de Marneffe, Marie-Catherine
%Y Meza Ruiz, Ivan Vladimir
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F zhong-etal-2022-less
%X Personalized dialogue systems explore the problem of generating responses that are consistent with the user‘s personality, which has raised much attention in recent years. Existing personalized dialogue systems have tried to extract user profiles from dialogue history to guide personalized response generation. Since the dialogue history is usually long and noisy, most existing methods truncate the dialogue history to model the user‘s personality. Such methods can generate some personalized responses, but a large part of dialogue history is wasted, leading to sub-optimal performance of personalized response generation. In this work, we propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more abundant and accurate persona information. Specifically, we design an MSP model which consists of three personal information refiners and a personalized response generator. With these multi-level refiners, we can sparsely extract the most valuable information (tokens) from the dialogue history and leverage other similar users’ data to enhance personalization. Experimental results on two real-world datasets demonstrate the superiority of our model in generating more informative and personalized responses.
%R 10.18653/v1/2022.naacl-main.426
%U https://aclanthology.org/2022.naacl-main.426/
%U https://doi.org/10.18653/v1/2022.naacl-main.426
%P 5808-5820
Markdown (Informal)
[Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation](https://aclanthology.org/2022.naacl-main.426/) (Zhong et al., NAACL 2022)
ACL