Multi-aspect Depression Severity Assessment via Inductive Dialogue System
This addresses the need for more nuanced depression detection in chatbots, though it appears incremental as it builds on prior binary or single-score methods.
The paper tackles the problem of assessing depression severity in patient conversations by introducing a multi-aspect evaluation task and an inductive dialogue system, with results showing potential based on a synthesized dataset and human evaluations.
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.