PAL : Persona-Augmented Emotional Support Conversation Generation (2023)

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 .

## introduction a growing number of people are experiencing mental health issues, particularly during the covid-19 pandemic @xcite @xcite , and more and more people are seeking mental health support. the high costs and limited availability of support provided by professional mental health supporters or counselors @xcite @xcite have highlighted the importance of employing conversational agents and chatbots for automating this task @xcite @xcite . towards this end, @xcite pioneered the task of emotional support conversation generation to reduce users' emotional distress and improve their mood using language models. they collected esconv, a high-quality crowd-sourced dataset of conversations (with annotated helping strategies) between support seekers and trained emotional supporters, and demonstrated that training large pretrained dialogue models on this dataset enabled these models to provide effective support. @xcite proposed to leverage commonsense knowledge and implemented hybrid strategies to improve the performance of dialogue models in this task. similarly, @xcite also suggested using commonsense knowledge for this task and further proposed a global-to-local graph network to model local and global hierarchical relationships. more recently, @xcite proposed look-ahead strategy planning to select strategies that are more effective for long-turn interactions. although previous studies have considered relevant psychological theories and factors, such as commonsense reasoning, they neglect information regarding the users' persona. persona, which can be considered as an outward expression of personality @xcite in psychology, is also closely related to empathy @xcite , anxiety @xcite , frustration (jeronimus and laceulle, 2017), mental health @xcite and distress @xcite , all of which are essential concepts in psychological scenarios. effective emotional support benefits from an adequate understanding of the support seeker's personality, as shown by research on person-centered therapy @xcite , while more specific and persona-related words lead to a long-term rapport with the user @xcite . thus, the inability to actively combine persona information and conversations prevents users from developing such rapport with the system @xcite , which is not desirable for emotional support. therefore, it is intuitive to explore seekers' personas and build systems for providing personalized emotional support. in this paper, we propose persona-augmented emotional support (pal), a conversational model that learns to dynamically leverage seekers' personas to generate more informative and personalized responses for effective emotional support. to more closely match realistic scenarios (no prior knowledge of the user's persona) and retain important user information from earlier conversation rounds, we first extract persona information about the seeker based on the conversation history and design an attention mechanism to enhance the understanding of the seeker. furthermore, we propose a strategy-based controllable generation method to actively incorporate persona information in responses for a better rapport with the user. we conduct our experiments on the esconv dataset @xcite . our results demonstrate that pal outperforms the baselines in automatic and manual evaluations, providing more personalized and effective emotional support. we summarize our contributions as follows: ## persona-augmented emotional support figure 2 shows the overall flow of our approach. we first infer the seeker's persona information from the conversation history and then leverage the inferred information to generate a response. our approach is comprised of three major components: the persona extractor for inferring the seeker's persona information ( §3.2); the response generator that leverages the inferred persona information and generates the response distribution ( §3.3); a strategy-based controllable decoding method for generating appropriate responses ( §3.4). figure 2 : the overall structure of persona-augmented emotional support (pal). we extract the seeker's persona from the dialogue history and then use a controllable generation method to generate the response. α is a tunable hyperparameter. ## case study in ## conclusion in this work, we introduced persona information into the emotional support task. we proposed a framework that can dynamically capture seekers' persona information, infer persona information using our trained persona extractor, and generate responses with a strategy-based controllable generation method. through extensive experiments, we demonstrated that our proposed approach outperformed the studied baselines in both human and manual evaluation. in addition, we provided persona annotations for the esconv dataset using the persona extractor model, which will foster the research of personalized emotional support conversations.