# Multi-aspect Depression Severity Assessment via Inductive Dialogue System

arXiv: https://arxiv.org/abs/2410.21836 · HTML: https://mlsanity.com/paper/2410.21836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 9 of 9)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge](https://mlsanity.com/paper/1605.01600/) (2016)
- [Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model](https://mlsanity.com/paper/2202.08210/) (2022)
- [Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring](https://mlsanity.com/paper/2305.16826/) (2023)
- [Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation](https://mlsanity.com/paper/2105.11696/) (2021)
- [Target Guided Emotion Aware Chat Machine](https://mlsanity.com/paper/2011.07432/) (2020)
- [Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN](https://mlsanity.com/paper/2202.12456/) (2022)
- [Multi-Task Learning for Depression Detection in Dialogs](https://mlsanity.com/paper/2208.10250/) (2022)

## Abstract (arXiv)

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.