Towards Automated Psychotherapy via Language Modeling
This work addresses the problem of providing accessible and stigma-reducing psychotherapy support, though it is incremental as it builds on existing NLG methods.
The researchers tackled automating psychotherapist-client text conversations using a Seq2Seq Transformer model trained on movie dialogues and a psychotherapeutic dataset, achieving performance that met or exceeded human-written responses in 59.7% and 67.1% of test cases across two benchmarks.
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.