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


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.
Anthology ID:
2022.bionlp-1.27
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Editors:
Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
285–297
Language:
URL:
https://aclanthology.org/2022.bionlp-1.27
DOI:
10.18653/v1/2022.bionlp-1.27
Bibkey:
Cite (ACL):
Avisha Das, Salih Selek, Alia R. Warner, Xu Zuo, Yan Hu, Vipina Kuttichi Keloth, Jianfu Li, W. Jim Zheng, and Hua Xu. 2022. Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 285–297, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues (Das et al., BioNLP 2022)
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