Working Alliance Transformer for Psychotherapy Dialogue Classification

Long been a clinical quantity estimated by the patients' (WAT), a transformer-based classification model to classify and therapists' self-evaluative reports, we believe that the the psychotherapy sessions into different psychiatric working alliance can be better characterized using natural conditions. Our methods consists of a psychological state language processing technique directly in the dialogue transcribed encoder that quantifies the degree of patient-therapist alliance in each therapy session. In this work, we propose the by projecting each turn in a therapeutic session onto Working Alliance Transformer (WAT), a Transformer-based the representation of clinically established working alliance classification model that has a psychological state encoder inventories, using language modeling to encode both turns which infers the working alliance scores by projecting the and inventories, which was originally proposed in [4] as an embedding of the dialogues turns onto the embedding space analytical tool. This allows us not only to quantify the overall of the clinical inventory for working alliance. We evaluate our degree of alliance but also to identify granular patterns its dynamics method in a real-world dataset with over 950 therapy sessions over shorter and longer time scales.