Conversational bots for psychotherapy: a study of generative transformer models using domain-specific dialogues

Avisha Das, Salih Selek, Alia R Warner, Xu Zuo, Yan Hu, Vipina Kuttichi Keloth, Jianfu Li, W Jim Zheng, Hua Xu
January, 2022
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Abstract

Conversational bots have become non-traditional methods for therapy among individuals suffering from psychological illnesses. Leveraging deep neural generative language models, we propose a deep trainable neural conversational model for therapy-oriented response generation. We leverage transfer learning methods during training on therapy and counseling based data from Reddit and AlexanderStreet. This was done to adapt existing generative models – GPT2 and DialoGPT – to the task of automated dialog generation. Through quantitative evaluation of the linguistic quality, we observe that the dialog generation model - DialoGPT (345M) with transfer learning on video data attains scores similar to a human response baseline. However, human evaluation of responses by conversational bots show mostly signs of generic advice or information sharing instead of therapeutic interaction.

Type
Conference paper
Publication
In ‘Proceedings of the 21st Workshop on Biomedical Language Processing’
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Avisha Das
Avisha Das
Research Fellow

My research interests include natural language understanding and generation with a focus on Biomedical NLP and AI Security.