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    <abstract>Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous studies have demonstrated that seekers’ persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support. In this paper, our empirical analysis verifies that persona has an important impact on emotional support. Therefore, we propose a framework for dynamically inferring and modeling seekers’ persona. We first train a model for inferring the seeker’s persona from the conversation history. Accordingly, we propose PAL, a model that leverages persona information and, in conjunction with our strategy-based controllable generation method, provides personalized emotional support. Automatic and manual evaluations demonstrate that PAL achieves state-of-the-art results, outperforming the baselines on the studied benchmark. Our code and data are publicly available at https://github.com/chengjl19/PAL.</abstract>
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%0 Conference Proceedings
%T PAL: Persona-Augmented Emotional Support Conversation Generation
%A Cheng, Jiale
%A Sabour, Sahand
%A Sun, Hao
%A Chen, Zhuang
%A Huang, Minlie
%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 cheng-etal-2023-pal
%X Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support. Recent work has demonstrated the effectiveness of dialogue models in providing emotional support. As previous studies have demonstrated that seekers’ persona is an important factor for effective support, we investigate whether there are benefits to modeling such information in dialogue models for support. In this paper, our empirical analysis verifies that persona has an important impact on emotional support. Therefore, we propose a framework for dynamically inferring and modeling seekers’ persona. We first train a model for inferring the seeker’s persona from the conversation history. Accordingly, we propose PAL, a model that leverages persona information and, in conjunction with our strategy-based controllable generation method, provides personalized emotional support. Automatic and manual evaluations demonstrate that PAL achieves state-of-the-art results, outperforming the baselines on the studied benchmark. Our code and data are publicly available at https://github.com/chengjl19/PAL.
%R 10.18653/v1/2023.findings-acl.34
%U https://aclanthology.org/2023.findings-acl.34
%U https://doi.org/10.18653/v1/2023.findings-acl.34
%P 535-554

Markdown (Informal)

[PAL: Persona-Augmented Emotional Support Conversation Generation](https://aclanthology.org/2023.findings-acl.34) (Cheng et al., Findings 2023)

• PAL: Persona-Augmented Emotional Support Conversation Generation (Cheng et al., Findings 2023)

ACL

• Jiale Cheng, Sahand Sabour, Hao Sun, Zhuang Chen, and Minlie Huang. 2023. PAL: Persona-Augmented Emotional Support Conversation Generation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 535–554, Toronto, Canada. Association for Computational Linguistics.