Towards Automated Psychotherapy via Language Modeling

• URL: http://arxiv.org/abs/2104.10661v1
• Date: Mon, 5 Apr 2021 01:53:39 GMT
• Title: Towards Automated Psychotherapy via Language Modeling
• Authors: Houjun Liu
• Abstract summary: The model was trained 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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