PsyChat: A Client-Centric Dialogue System for Mental Health Support

• URL: http://arxiv.org/abs/2312.04262v2
• Date: Wed, 20 Mar 2024 01:59:39 GMT
• Title: PsyChat: A Client-Centric Dialogue System for Mental Health Support
• Authors: Huachuan Qiu, Anqi Li, Lizhi Ma, Zhenzhong Lan,
• Abstract summary: PsyChat is a client-centric dialogue system that provides psychological support through online chat. It comprises five modules: client behavior recognition, counselor strategy selection, input packer, response generator, and response selection. Case study demonstrates that the dialogue system can predict the client's behaviors, select appropriate counselor strategies, and generate accurate and suitable responses.
• Score: 16.008761874266728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dialogue systems are increasingly integrated into mental health support to help clients facilitate exploration, gain insight, take action, and ultimately heal themselves. A practical and user-friendly dialogue system should be client-centric, focusing on the client's behaviors. However, existing dialogue systems publicly available for mental health support often concentrate solely on the counselor's strategies rather than the behaviors expressed by clients. This can lead to unreasonable or inappropriate counseling strategies and corresponding responses generated by the dialogue system. To address this issue, we propose PsyChat, a client-centric dialogue system that provides psychological support through online chat. The client-centric dialogue system comprises five modules: client behavior recognition, counselor strategy selection, input packer, response generator, and response selection. Both automatic and human evaluations demonstrate the effectiveness and practicality of our proposed dialogue system for real-life mental health support. Furthermore, the case study demonstrates that the dialogue system can predict the client's behaviors, select appropriate counselor strategies, and generate accurate and suitable responses.

Related papers

• Investigating the effect of Mental Models in User Interaction with an Adaptive Dialog Agent [27.75972750138208]
  Mental models play an important role in whether user interaction with intelligent systems is successful or not. We show that users have a variety of conflicting mental models about such systems. We show that adapting a dialog agent's behavior to better align with users' mental models, even when done implicitly, can improve perceived usability, dialog efficiency, and success.
  arXiv  Detail & Related papers  (2024-08-26T09:57:19Z)
• Emotion Talk: Emotional Support via Audio Messages for Psychological Assistance [0.0]
  "Emotion Talk" is a system designed to provide continuous emotional support through audio messages for psychological assistance. The solution focuses on Portuguese-speaking users, ensuring that the system is linguistically and culturally relevant.
  arXiv  Detail & Related papers  (2024-07-12T05:13:17Z)
• Can Large Language Models be Used to Provide Psychological Counselling? An Analysis of GPT-4-Generated Responses Using Role-play Dialogues [0.0]
  Mental health care poses an increasingly serious challenge to modern societies. This study collected counseling dialogue data via role-playing scenarios involving expert counselors. Third-party counselors evaluated the appropriateness of responses from human counselors and those generated by GPT-4 in identical contexts.
  arXiv  Detail & Related papers  (2024-02-20T06:05:36Z)
• K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning [83.19215082550163]
  We propose K-ESConv, a novel prompt learning based knowledge injection method for emotional support dialogue system. We evaluate our model on an emotional support dataset ESConv, where the model retrieves and incorporates knowledge from external professional emotional Q&A forum.
  arXiv  Detail & Related papers  (2023-12-16T08:10:10Z)
• Response-act Guided Reinforced Dialogue Generation for Mental Health Counseling [25.524804770124145]
  We present READER, a dialogue-act guided response generator for mental health counseling conversations. READER is built on transformer to jointly predict a potential dialogue-act d(t+1) for the next utterance (aka response-act) and to generate an appropriate response u(t+1) We evaluate READER on HOPE, a benchmark counseling conversation dataset.
  arXiv  Detail & Related papers  (2023-01-30T08:53:35Z)
• Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning [81.79431311952656]
  We propose a novel system MultiESC to provide Emotional Support. For strategy planning, we propose lookaheads to estimate the future user feedback after using particular strategies. For user state modeling, MultiESC focuses on capturing users' subtle emotional expressions and understanding their emotion causes.
  arXiv  Detail & Related papers  (2022-10-09T12:23:47Z)
• ProsocialDialog: A Prosocial Backbone for Conversational Agents [104.92776607564583]
  We introduce ProsocialDialog, the first large-scale dialogue dataset to teach conversational agents to respond to problematic content following social norms. Created via a human-AI collaborative framework, ProsocialDialog consists of 58K dialogues, with 331K utterances, 160K RoTs, and 497K dialogue safety labels. With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost.
  arXiv  Detail & Related papers  (2022-05-25T11:48:47Z)
• Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems [109.16553492049441]
  We propose a novel method to incorporate the knowledge reasoning capability into dialogue systems in a more scalable and generalizable manner. To the best of our knowledge, this is the first work to have transformer models generate responses by reasoning over differentiable knowledge graphs.
  arXiv  Detail & Related papers  (2022-03-20T17:51:49Z)
• Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations [15.230185998553159]
  We develop a novel dataset, named HOPE, to provide a platform for the dialogue-act classification in counselling conversations. We collect 12.9K utterances from publicly-available counselling session videos on YouTube, extract their transcripts, clean, and annotate them with DAC labels. We propose SPARTA, a transformer-based architecture with a novel speaker- and time-aware contextual learning for the dialogue-act classification.
  arXiv  Detail & Related papers  (2021-11-12T10:30:30Z)
• Towards Emotional Support Dialog Systems [61.58828606097423]
  We define the Emotional Support Conversation task and propose an ESC Framework, which is grounded on the Helping Skills Theory. We construct an Emotion Support Conversation dataset (ESConv) with rich annotation (especially support strategy) in a help-seeker and supporter mode. We evaluate state-of-the-art dialog models with respect to the ability to provide emotional support.
  arXiv  Detail & Related papers  (2021-06-02T13:30:43Z)
• Attention over Parameters for Dialogue Systems [69.48852519856331]
  We learn a dialogue system that independently parameterizes different dialogue skills, and learns to select and combine each of them through Attention over Parameters (AoP) The experimental results show that this approach achieves competitive performance on a combined dataset of MultiWOZ, In-Car Assistant, and Persona-Chat.
  arXiv  Detail & Related papers  (2020-01-07T03:10:42Z)

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.