  As a predictive measure of the treatment outcome in psychotherapy, the
working alliance measures the agreement of the patient and the therapist in
terms of their bond, task and goal. Long been a clinical quantity estimated by
the patients' and therapists' self-evaluative reports, we believe that the
working alliance can be better characterized using natural language processing
technique directly in the dialogue transcribed in each therapy session. In this
work, we propose the Working Alliance Transformer (WAT), a Transformer-based
classification model that has a psychological state encoder which infers the
working alliance scores by projecting the embedding of the dialogues turns onto
the embedding space of the clinical inventory for working alliance. We evaluate
our method in a real-world dataset with over 950 therapy sessions with anxiety,
depression, schizophrenia and suicidal patients and demonstrate an empirical
advantage of using information about the therapeutic states in this sequence
classification task of psychotherapy dialogues.
