Multi-aspect Depression Severity Assessment via Inductive Dialogue System

TL;DR

This paper introduces MaDSA, a novel multi-aspect depression assessment system using dialogue responses and emotion classification, aiming to improve automatic depression detection in patient conversations.

Contribution

It presents a new multi-aspect depression severity assessment task with a hierarchical structure and an auxiliary emotion classification, along with a synthesized dataset.

Findings

Demonstrates the feasibility of multi-aspect depression assessment

Shows robustness of the dataset through human evaluations

Preliminary results indicate potential for improved depression detection

Abstract

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.