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

Abstract

Conversational bots have become nontraditional methods for therapy among individuals suffering from psychological illnesses. Leveraging deep neural generative language models, we propose a deep trainable neural conversational model for therapyoriented 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.