论文标题
PAL：角色增强的情感支持对话产生
PAL: Persona-Augmented Emotional Support Conversation Generation
论文作者
论文摘要
由于缺乏人力资源来获得心理健康支持，因此对使用对话代理进行支持的需求不断增加。最近的工作证明了对话模型在提供情感支持方面的有效性。由于以前的研究表明，寻求者的角色是有效支持的重要因素，因此我们研究在对话模型中为支持模型建模是否有好处。在本文中，我们的经验分析验证了角色对情感支持有重要影响。因此，我们为动态推断和建模寻求者的角色提供了一个框架。我们首先训练一个模型，以从对话历史中推断出寻求者的角色。因此，我们提出了一个模型，该模型利用角色信息，并与我们的基于策略的可控生成方法一起提供了个性化的情感支持。自动和手动评估表明，PAL取得了最先进的结果，表现优于研究基准上的基准。我们的代码和数据可在https://github.com/chengjl19/pal上公开获取。
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.
