Multi-aspect Depression Severity Assessment via Inductive Dialogue System

• URL: http://arxiv.org/abs/2410.21836v1
• Date: Tue, 29 Oct 2024 08:00:08 GMT
• Title: Multi-aspect Depression Severity Assessment via Inductive Dialogue System
• Authors: Chaebin Lee, Seungyeon Seo, Heejin Do, Gary Geunbae Lee,
• Abstract summary: We present a novel task of multi-aspect depression severity assessment via an inductive dialogue system (MaDSA) We propose a foundational system for MaDSA, which induces psychological dialogue responses with an auxiliary emotion classification task. We synthesize the conversational dataset annotated with eight aspects of depression severity alongside emotion labels, proven robust via human evaluations.
• Score: 5.156059061769101
• License:
• 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.

Related papers

• DiagESC: Dialogue Synthesis for Integrating Depression Diagnosis into Emotional Support Conversation [4.795837146925278]
  We introduce the Diagnostic Emotional Support Conversation task for an advanced mental health management system. We develop the DESC dataset to assess depression symptoms while maintaining user experience.
  arXiv  Detail & Related papers  (2024-08-12T10:26:39Z)
• HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection [11.984035389013426]
  HiQuE is a novel depression detection framework that leverages the hierarchical relationship between primary and follow-up questions in clinical interviews. We conduct extensive experiments on the widely-used clinical interview data, DAIC-WOZ, where our model outperforms other state-of-the-art multimodal depression detection models.
  arXiv  Detail & Related papers  (2024-08-07T09:23:01Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• Speech-based Clinical Depression Screening: An Empirical Study [32.84863235794086]
  This study investigates the utility of speech signals for AI-based depression screening across varied interaction scenarios. participants include depressed patients recruited from the outpatient clinics of Peking University Sixth Hospital. We extracted acoustic and deep speech features from each participant's segmented recordings.
  arXiv  Detail & Related papers  (2024-06-05T09:43:54Z)
• Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
  We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system. We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context. Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
  arXiv  Detail & Related papers  (2024-04-03T08:48:50Z)
• Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking [27.96718892323191]
  Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection. Recent work focuses on combining task-oriented dialogue and chitchat to simulate the interview-based depression diagnosis. No explicit framework has been explored to guide the dialogue, which results in some useless communications.
  arXiv  Detail & Related papers  (2024-03-12T07:17:01Z)
• CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation [59.8935454665427]
  Empathetic dialogue models usually consider only the affective aspect or treat cognition and affection in isolation. We propose the CASE model for empathetic dialogue generation.
  arXiv  Detail & Related papers  (2022-08-18T14:28:38Z)
• D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat [25.852922703368133]
  In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms. Due to the social stigma associated with mental illness, the dialogue data related to depression consultation and diagnosis are rarely disclosed. We construct a Chinese dialogue dataset for Depression-Diagnosis-Oriented Chat which simulates the dialogue between doctors and patients during the diagnosis of depression.
  arXiv  Detail & Related papers  (2022-05-24T03:54:22Z)
• Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes [50.569762345799354]
  We argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label.
  arXiv  Detail & Related papers  (2021-09-18T04:22:49Z)
• Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach [84.02388020258141]
  We propose a new framework named ENIGMA for estimating human evaluation scores based on off-policy evaluation in reinforcement learning. ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation. Our experiments show that ENIGMA significantly outperforms existing methods in terms of correlation with human evaluation scores.
  arXiv  Detail & Related papers  (2021-02-20T03:29:20Z)
• You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
  The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation. Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
  arXiv  Detail & Related papers  (2020-04-11T12:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.