Deep Learning Mental Health Dialogue System

Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models designed for mental health counseling could help improve outcomes by lowering barriers to access. To this end, we have deve...

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Bibliographic Details
Published in:International Conference on Big Data and Smart Computing pp. 395 - 398
Main Authors: Brocki, Lennart, Dyer, George C., Gladka, Anna, Chung, Neo Christopher
Format: Conference Proceeding
Language:English
Published: IEEE 01.02.2023
Subjects:
Artificial Intelligence
Big Data
Chatbot
Costs
Deep learning
Dialogue System
Employee welfare
Mental health
Transformer cores
Transformers
ISSN:2375-9356
Online Access:Get full text
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Summary:Mental health counseling remains a major challenge in modern society due to cost, stigma, fear, and unavailability. We posit that generative artificial intelligence (AI) models designed for mental health counseling could help improve outcomes by lowering barriers to access. To this end, we have developed a deep learning (DL) dialogue system called Serena. The system consists of a core generative model and post-processing algorithms. The core generative model is a 2.7 billion parameter Seq2Seq Transformer [26] fine-tuned on thousands of transcripts of person-centered-therapy (PCT) sessions. The series of postprocessing algorithms detects contradictions, improves coherency, and removes repetitive answers. Serena is implemented and deployed on https://serena.chat, which currently offers limited free services. While the dialogue system is capable of responding in a qualitatively empathetic and engaging manner, occasionally it displays hallucination and long-term incoherence. Overall, we demonstrate that a deep learning mental health dialogue system has the potential to provide a low-cost and effective complement to traditional human counselors with less barriers to access.
ISSN:2375-9356
DOI:10.1109/BigComp57234.2023.00097