PsyChat: A Client-Centric Dialogue System for Mental Health Support

Abstract

Dialogue systems are increasingly integrated into mental health support to help clients facilitate exploration, gain insight, take action, and ultimately heal themselves. A practical and user-friendly dialogue system should be client-centric, focusing on the client's behaviors. However, existing dialogue systems publicly available for mental health support often concentrate solely on the counselor's strategies rather than the behaviors expressed by clients. This can lead to unreasonable or inappropriate counseling strategies and corresponding responses generated by the dialogue system. To address this issue, we propose PsyChat, a client-centric dialogue system that provides psychological support through online chat. The client-centric dialogue system comprises five modules: client behavior recognition, counselor strategy selection, input packer, response generator, and response selection. Both automatic and human evaluations demonstrate the effectiveness and practicality of our proposed dialogue system for real-life mental health support. Furthermore, the case study demonstrates that the dialogue system can predict the client's behaviors, select appropriate counselor strategies, and generate accurate and suitable responses.

Figures (5)

• Figure 1: An illustration depicting how a counselor tailors strategies in response to the behaviors exhibited by the client.
• Figure 2: Architecture of the client-centric dialogue system. It contains two main parts: (a) Front-end web UI and (b) Back-end dialogue system, which is implemented with Huggingface and FastAPI packages.
• Figure 3: Mechanism of client behavior recognition.
• Figure 4: Template of the input packer. During the fine-tuning process, the ground truth $R_g$ is treated as a supervised signal. In the inference process, our goal is to generate the response.
• Figure 5: An illustration of a case study of our client-centric dialogue system.