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.

翻译：随着聊天机器人的进步及自动抑郁检测需求的增长，从患者对话中识别抑郁受到更多关注。然而，现有方法常以二元方式评估抑郁或仅提供单一评分，缺乏多样化反馈，且未重视增强对话响应。本文提出一种通过归纳式对话系统进行多维度抑郁严重程度评估的新任务，通过结合评估辅助的响应生成，在多维度标准下评估患者的抑郁水平。进一步，我们提出了该任务的基础系统，其在分层严重程度评估结构中，通过辅助情绪分类任务引导生成心理对话响应。我们构建了标注有八个抑郁严重程度维度及情绪标签的对话数据集，并通过人工评估验证了其稳健性。实验结果表明我们在该任务上的初步工作具有潜力。