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        <title>Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description</title>
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        <namePart type="given">Wangchunshu</namePart>
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    <abstract>Personalizing dialogue agents is important for dialogue systems to generate more specific,consistent, and engaging responses. However, most current dialogue personalization approaches rely on explicit persona descriptions during inference, which severely restricts its application. In this paper, we propose a novel approach that learns to predict persona information based on the dialogue history to personalize the dialogue agent without relying on any explicit persona descriptions during inference. Experimental results on the PersonaChat dataset show that the proposed method can improve the consistency of generated responses when conditioning on the predicted profile of the dialogue agent (i.e. “self persona”), and improve the engagingness of the generated responses when conditioning on the predicted persona of the dialogue partner (i.e. “their persona”). We also find that a trained persona prediction model can be successfully transferred to other datasets and help generate more relevant responses.</abstract>
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%0 Conference Proceedings
%T Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description
%A Zhou, Wangchunshu
%A Li, Qifei
%A Li, Chenle
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhou-etal-2023-learning
%X Personalizing dialogue agents is important for dialogue systems to generate more specific,consistent, and engaging responses. However, most current dialogue personalization approaches rely on explicit persona descriptions during inference, which severely restricts its application. In this paper, we propose a novel approach that learns to predict persona information based on the dialogue history to personalize the dialogue agent without relying on any explicit persona descriptions during inference. Experimental results on the PersonaChat dataset show that the proposed method can improve the consistency of generated responses when conditioning on the predicted profile of the dialogue agent (i.e. “self persona”), and improve the engagingness of the generated responses when conditioning on the predicted persona of the dialogue partner (i.e. “their persona”). We also find that a trained persona prediction model can be successfully transferred to other datasets and help generate more relevant responses.
%R 10.18653/v1/2023.findings-acl.186
%U https://aclanthology.org/2023.findings-acl.186
%U https://doi.org/10.18653/v1/2023.findings-acl.186
%P 2979-2991

Markdown (Informal)

[Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description](https://aclanthology.org/2023.findings-acl.186) (Zhou et al., Findings 2023)

• Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description (Zhou et al., Findings 2023)

ACL

• Wangchunshu Zhou, Qifei Li, and Chenle Li. 2023. Learning to Predict Persona Information for Dialogue Personalization without Explicit Persona Description. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2979–2991, Toronto, Canada. Association for Computational Linguistics.