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.

翻译：由于心理健康支持的人力资源短缺，对话式智能体在情感支持中的应用需求日益增长。近期研究表明，对话模型在提供情感支持方面具有显著效果。鉴于已有研究证实寻求者的人格特征是有效支持的重要因素，我们探究了在对话支持模型中建模此类信息的价值。本文通过实证分析验证了人格特征对情感支持具有重要影响。为此，我们提出了一种动态推断与建模寻求者人格特征的框架。首先训练模型从对话历史中推断寻求者人格特征，进而提出PAL模型，该模型融合人格信息，并结合基于策略的可控生成方法，提供个性化情感支持。自动评估与人工评估结果显示，PAL在基准测试中达到了最优性能，显著优于基线模型。我们的代码与数据集已开源至https://github.com/chengjl19/PAL。