STAMPsy: Towards SpatioTemporal-Aware Mixed-Type Dialogues for Psychological Counseling

TL;DR

This paper introduces STAMPsy, a comprehensive dataset and framework for mixed-type psychological counseling dialogues that incorporate spatiotemporal awareness to improve system understanding and effectiveness.

Contribution

It presents the first large-scale mixed-type counseling dialogue dataset with spatiotemporal context and a novel self-feedback generation framework for enhanced psychological dialogue systems.

Findings

Clarifying dialogue goals improves system performance.

Spatiotemporal-aware knowledge enhances mental health understanding.

Self-STAMPsy outperforms baseline models in dialogue quality.

Abstract

Online psychological counseling dialogue systems are trending, offering a convenient and accessible alternative to traditional in-person therapy. However, existing psychological counseling dialogue systems mainly focus on basic empathetic dialogue or QA with minimal professional knowledge and without goal guidance. In many real-world counseling scenarios, clients often seek multi-type help, such as diagnosis, consultation, therapy, console, and common questions, but existing dialogue systems struggle to combine different dialogue types naturally. In this paper, we identify this challenge as how to construct mixed-type dialogue systems for psychological counseling that enable clients to clarify their goals before proceeding with counseling. To mitigate the challenge, we collect a mixed-type counseling dialogues corpus termed STAMPsy, covering five dialogue types, task-oriented dialogue for diagnosis, knowledge-grounded dialogue, conversational recommendation, empathetic dialogue, and question answering, over 5,000 conversations. Moreover, spatiotemporal-aware knowledge enables systems to have world awareness and has been proven to affect one's mental health. Therefore, we link dialogues in STAMPsy to spatiotemporal state and propose a spatiotemporal-aware mixed-type psychological counseling dataset. Additionally, we build baselines on STAMPsy and develop an iterative self-feedback psychological dialogue generation framework, named Self-STAMPsy. Results indicate that clarifying dialogue goals in advance and utilizing spatiotemporal states are effective.