mlsanityData to 14 Oct 2026

Working Alliance Transformer for Psychotherapy Dialogue Classification

arXiv 2210.15603 ↗First posted 27 Oct 2022cs.CL cs.AI cs.HCcited by 9

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 11 of its 24 references that are in the map. First posted on arXiv 27 Oct 2022.

Builds on

11 of 24 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2014Distributed Representations of Sentences and Documentscited by 678
  4. 2022Deep Annotation of Therapeutic Working Alliance in Psychotherapycited by 14
  5. 2019A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatrycited by 13
  6. 2022Neural Topic Modeling of Psychotherapy Sessionscited by 13
  7. 2020Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RLcited by 11
  8. 2020Predicting human decision making in psychological tasks with recurrent neural networkscited by 11
  9. 2019Split Q Learning: Reinforcement Learning with Two-Stream Rewardscited by 8
  10. 2022SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learningcited by 8
  11. 2022Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlookcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 0; 2026: 2.

9 papers.

  1. 2024Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMscited by 19
  2. 2023Towards Healthy AI: Large Language Models Need Therapists Toocited by 13
  3. 2024COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modelingcited by 8
  4. 2023Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamicscited by 6
  5. 2023Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosiscited by 4
  6. 2023TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Artscited by 3
  7. 2024Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Modelscited by 1
  8. 2026GenAI for Social Work Field Education: Client Simulation with Real-Time Feedbackcited by 0
  9. 2026CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Modelscited by 0