通过对话系统多维度评估抑郁程度，提升反馈实用性。
Multi-aspect Depression Severity Assessment via Inductive Dialogue System
- 构建分层评估结构，融合情绪分类生成心理对话回复。
- 在8个抑郁维度上标注数据集，经人工验证效果稳健。
- 适合心理健康对话系统研究者与临床辅助工具开发者。
随着聊天机器人发展和自动抑郁检测需求增长，从患者对话中识别抑郁日益受到关注。然而，以往方法多采用二分类或单一评分，缺乏多样化反馈且未重视对话响应质量。本文提出一种新型任务——基于归纳式对话系统的多维度抑郁严重程度评估（MaDSA），通过引入评估辅助的回复生成机制，在多个维度上评估患者抑郁水平。我们构建了首个面向MaDSA的基础系统，利用分层严重度评估结构中的辅助情绪分类任务，生成符合心理干预逻辑的对话回复。同时，我们合成一个包含8个抑郁维度严重度及情绪标签的对话数据集，经人工评估证明其可靠性。实验表明，该初步工作在多维度抑郁评估方面展现出潜力。
原文摘要 · Abstract (English)
With the advancement of chatbots and the growing demand for automatic depression detection, identifying depression in patient conversations has gained more attention. However, prior methods often assess depression in a binary way or only a single score without diverse feedback and lack focus on enhancing dialogue responses. In this paper, we present a novel task of multi-aspect depression severity assessment via an inductive dialogue system (MaDSA), evaluating a patient's depression level on multiple criteria by incorporating an assessment-aided response generation. Further, we propose a foundational system for MaDSA, which induces psychological dialogue responses with an auxiliary emotion classification task within a hierarchical severity assessment structure. We synthesize the conversational dataset annotated with eight aspects of depression severity alongside emotion labels, proven robust via human evaluations. Experimental results show potential for our preliminary work on MaDSA.
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