PsyChat: A Client-Centric Dialogue System for Mental Health Support
This work addresses the need for more appropriate and user-friendly AI support in mental health care, though it appears incremental as it builds on existing dialogue system frameworks.
The authors tackled the problem of existing mental health dialogue systems focusing too much on counselor strategies rather than client behaviors, which can lead to inappropriate responses, by proposing PsyChat, a client-centric system that includes modules for behavior recognition and strategy selection, with evaluations showing its effectiveness and practicality for real-life support.
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.