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.

As awareness of mental health issues grows, online counseling support services are becoming increasingly prevalent worldwide.Detecting whether users express suicidal ideation in textbased counseling services is crucial for identifying and prioritizing at-risk individuals.However, the lack of domain-specific systems to facilitate fine-grained suicide detection and corresponding risk assessment in online counseling poses a significant challenge for automated crisis intervention aimed at suicide prevention.In this paper, we propose PsyGUARD, an automated system for detecting suicide ideation and assessing risk in psychological counseling.To achieve this, we first develop a detailed taxonomy for detecting suicide ideation based on foundational theories.We then curate a largescale, high-quality dataset called PsySUICIDE for suicide detection.To evaluate the capabilities of automated systems in fine-grained suicide detection, we establish a range of baselines.Subsequently, to assist automated services in providing safe, helpful, and tailored responses for further assessment, we propose to build a suite of risk assessment frameworks.Our study not only provides an insightful analysis of the effectiveness of automated risk assessment systems based on fine-grained suicide detection but also highlights their potential to improve mental health services on online counseling platforms.Code, data, and models are available at https://github.com/qiuhuachuan/ PsyGUARD.

Anqi Li, Lizhi Ma, Yaling Mei, Hongliang He, Shuai Zhang, Huachuan Qiu, Zhenzhong Lan. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.