基于归纳式对话系统的多维度抑郁严重程度评估
Multi-aspect Depression Severity Assessment via Inductive Dialogue System
AI总结：
针对现有抑郁评估方法维度单一、缺乏对话优化的问题，本文提出MaDSA多维度抑郁评估任务及基础系统，结合分层评估结构与辅助情感分类生成对话，构建的多维度标注数据集经人工验证有效，初步实验显示出应用潜力。
摘要：
随着聊天机器人技术的发展以及自动抑郁检测需求的增长，从患者对话中识别抑郁受到了越来越多的关注。然而，现有方法通常采用二元方式评估抑郁，或仅给出单一评分，缺乏多样化反馈，且未重点关注对话回复质量的提升。本文提出了一项基于归纳式对话系统的多维度抑郁严重程度评估（MaDSA）新任务，通过结合评估辅助的回复生成，依据多项标准评估患者的抑郁水平。此外，我们构建了MaDSA的基础系统，该系统在分层严重程度评估结构中，借助辅助情感分类任务生成心理对话回复。我们合成了标注有8个抑郁严重程度维度及情感标签的对话数据集，经人工评估验证其具有鲁棒性。实验结果表明，我们的MaDSA初步工作具备应用潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
