Towards Reliable and Empathetic Depression-Diagnosis-Oriented Chats
- URL: http://arxiv.org/abs/2404.05012v1
- Date: Sun, 7 Apr 2024 16:35:53 GMT
- Title: Towards Reliable and Empathetic Depression-Diagnosis-Oriented Chats
- Authors: Kunyao Lan, Cong Ming, Binwei Yao, Lu Chen, Mengyue Wu,
- Abstract summary: We propose an innovative definition and generation framework tailored explicitly for depression diagnosis dialogues.
The framework combines the reliability of task-oriented conversations with the appeal of empathy-related chit-chat.
Exhaustive experimental results indicate significant improvements in task completion and emotional support generation in depression diagnosis.
- Score: 15.36217265716081
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Chatbots can serve as a viable tool for preliminary depression diagnosis via interactive conversations with potential patients. Nevertheless, the blend of task-oriented and chit-chat in diagnosis-related dialogues necessitates professional expertise and empathy. Such unique requirements challenge traditional dialogue frameworks geared towards single optimization goals. To address this, we propose an innovative ontology definition and generation framework tailored explicitly for depression diagnosis dialogues, combining the reliability of task-oriented conversations with the appeal of empathy-related chit-chat. We further apply the framework to D$^4$, the only existing public dialogue dataset on depression diagnosis-oriented chats. Exhaustive experimental results indicate significant improvements in task completion and emotional support generation in depression diagnosis, fostering a more comprehensive approach to task-oriented chat dialogue system development and its applications in digital mental health.
Related papers
- Depression Diagnosis Dialogue Simulation: Self-improving Psychiatrist with Tertiary Memory [35.41386783586689]
This paper introduces the Agent Mental Clinic (AMC), a self-improving conversational agent system designed to enhance depression diagnosis through simulated dialogues between patient and psychiatrist agents.
We design a psychiatrist agent consisting of a tertiary memory structure, a dialogue control and a memory sampling module, fully leveraging the skills reflected by the psychiatrist agent, achieving great accuracy on depression risk and suicide risk diagnosis via conversation.
arXiv Detail & Related papers (2024-09-20T14:25:08Z) - 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) - 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) - Empowering Psychotherapy with Large Language Models: Cognitive
Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting.
DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas.
Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
arXiv Detail & Related papers (2023-10-11T02:47:21Z) - Building Emotional Support Chatbots in the Era of LLMs [64.06811786616471]
We introduce an innovative methodology that synthesizes human insights with the computational prowess of Large Language Models (LLMs)
By utilizing the in-context learning potential of ChatGPT, we generate an ExTensible Emotional Support dialogue dataset, named ExTES.
Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions.
arXiv Detail & Related papers (2023-08-17T10:49:18Z) - MDDial: A Multi-turn Differential Diagnosis Dialogue Dataset with
Reliability Evaluation [46.82607230465541]
Building end-to-end ADD dialogue systems requires dialogue training datasets.
There is no publicly available ADD dialogue dataset in English.
We introduce MDDial, the first differential diagnosis dialogue dataset in English.
arXiv Detail & Related papers (2023-08-16T04:56:55Z) - Read, Diagnose and Chat: Towards Explainable and Interactive
LLMs-Augmented Depression Detection in Social Media [37.473604649521945]
This paper proposes a new depression detection system based on LLMs that is both interpretable and interactive.
It not only provides a diagnosis, but also diagnostic evidence and personalized recommendations based on natural language dialogue with the user.
arXiv Detail & Related papers (2023-05-09T02:49:09Z) - A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects [100.75759050696355]
We provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues.
We discuss challenges that meet the real-world application needs but require a greater research focus in the future.
arXiv Detail & Related papers (2023-05-04T11:38:49Z) - 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) - Knowledge Bridging for Empathetic Dialogue Generation [52.39868458154947]
Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history.
We propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation.
arXiv Detail & Related papers (2020-09-21T09:21:52Z) - 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.