6th International Workshop on Dialog Systems (IWDS); 10th IEEE
International Conference on Big Data and Smart Computing (2022 BigComp)
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 fine-tuned on thousands of
transcripts of person-centered-therapy (PCT) sessions. The series of
post-processing algorithms detects contradictions, improves coherency, and
removes repetitive answers. Serena is implemented and deployed on
\url{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.
Lennart Brocki, George C. Dyer, Anna Gładka, Neo Christopher Chung
2023-01-23
