Working Alliance Transformer for Psychotherapy Dialogue Classification

Baihan Lin, Guillermo Cecchi, Djallel Bouneffouf

Abstract
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 therapeutic states in the sequence classification task of psychotherapy dialogues.
Anthology ID:
2024.clinicalnlp-1.6
Volume:
Proceedings of the 6th Clinical Natural Language Processing Workshop
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, Danielle Bitterman
Venues:
ClinicalNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
64–69
Language:
URL:
https://aclanthology.org/2024.clinicalnlp-1.6
DOI:
10.18653/v1/2024.clinicalnlp-1.6
Bibkey:
Cite (ACL):
Baihan Lin, Guillermo Cecchi, and Djallel Bouneffouf. 2024. Working Alliance Transformer for Psychotherapy Dialogue Classification. In Proceedings of the 6th Clinical Natural Language Processing Workshop, pages 64–69, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Working Alliance Transformer for Psychotherapy Dialogue Classification (Lin et al., ClinicalNLP-WS 2024)
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PDF:
https://preview.aclanthology.org/ingest-2024-clasp/2024.clinicalnlp-1.6.pdf
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