DiagESC: Dialogue Synthesis for Integrating Depression Diagnosis into Emotional Support Conversation

TL;DR

This paper presents DiagESC, a dialogue system designed to identify depression symptoms during emotional support conversations, enhancing mental health management with a new dataset and evaluation methods.

Contribution

Introduction of the DESC dataset and task for diagnosing depression in emotional support dialogues, with validation by psychological experts.

Findings

DESC outperforms existing data in depression diagnosis accuracy.

The system maintains fluent, coherent, and consistent conversations.

Professional evaluations confirm the dataset's effectiveness.

Abstract

Dialogue systems for mental health care aim to provide appropriate support to individuals experiencing mental distress. While extensive research has been conducted to deliver adequate emotional support, existing studies cannot identify individuals who require professional medical intervention and cannot offer suitable guidance. We introduce the Diagnostic Emotional Support Conversation task for an advanced mental health management system. We develop the DESC dataset to assess depression symptoms while maintaining user experience by utilizing task-specific utterance generation prompts and a strict filtering algorithm. Evaluations by professional psychological counselors indicate that DESC has a superior ability to diagnose depression than existing data. Additionally, conversational quality evaluation reveals that DESC maintains fluent, consistent, and coherent dialogues.