Deep Annotation of Therapeutic Working Alliance in Psychotherapy
Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf

TL;DR
This paper introduces a deep learning framework to infer the therapeutic working alliance directly from psychotherapy session transcripts, enabling real-time assessment and insights into patient-therapist interactions.
Contribution
It presents a novel method using deep embeddings to analyze psychotherapy dialogues at turn-level resolution, improving real-time assessment of the working alliance.
Findings
Effective mapping of patient-therapist alignment trajectories
Real-time analysis capability demonstrated on over 950 sessions
Provides interpretable insights for clinical psychiatry
Abstract
The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment. In practice, the working alliance is estimated from a set of scoring questionnaires in an inventory that both the patient and the therapists fill out. In this work, we propose an analytical framework of directly inferring the therapeutic working alliance from the natural language within the psychotherapy sessions in a turn-level resolution with deep embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each psychotherapy session can be transcribed and generated in real-time from the session speech recordings, and these embedded dialogues are compared with the distributed representations of the statements in the working alliance inventory. We demonstrate, in a real-world dataset with over 950 sessions of psychotherapy treatments in anxiety, depression,…
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Taxonomy
TopicsMental Health via Writing · Digital Mental Health Interventions · Mental Health Research Topics
