  In this experiment, a model was devised, trained, and evaluated to automate
psychotherapist/client text conversations through the use of state-of-the-art,
Seq2Seq Transformer-based Natural Language Generation (NLG) systems. Through
training the model upon a mix of the Cornell Movie Dialogue Corpus for language
understanding and an open-source, anonymized, and public licensed
psychotherapeutic dataset, the model achieved statistically significant
performance in published, standardized qualitative benchmarks against
human-written validation data - meeting or exceeding human-written responses'
performance in 59.7% and 67.1% of the test set for two independent test methods
respectively. Although the model cannot replace the work of psychotherapists
entirely, its ability to synthesize human-appearing utterances for the majority
of the test set serves as a promising step towards communizing and easing
stigma at the psychotherapeutic point-of-care.
