Conversational Bots for Psychotherapy: A Study of Generative Transformer Models Using Domain-specific Dialogues

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.