mlsanityData to 7 Oct 2026

Deep Annotation of Therapeutic Working Alliance in Psychotherapy

arXiv 2204.05522 ↗First posted 12 Apr 2022q-bio.NC cs.AI cs.CLcited by 14

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Abstract (arXiv)

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, schizophrenia and suicidal patients, the effectiveness of this method in mapping out trajectories of patient-therapist alignment and the interpretability that can offer insights in clinical psychiatry. We believe such a framework can be provide timely feedback to the therapist regarding the quality of the conversation in interview sessions.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 12 Apr 2022.

Builds on

5 of 14 references.

References · 5

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2014Distributed Representations of Sentences and Documentscited by 677
  3. 2019A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatrycited by 13
  4. 2020Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RLcited by 11
  5. 2020Predicting human decision making in psychological tasks with recurrent neural networkscited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 4; 2024: 2; 2025: 0; 2026: 2.

14 papers.

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  1. 2022Neural Topic Modeling of Psychotherapy Sessionscited by 13
  2. 2023Towards Healthy AI: Large Language Models Need Therapists Toocited by 13
  3. 2022Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlookcited by 10
  4. 2022Working Alliance Transformer for Psychotherapy Dialogue Classificationcited by 9
  5. 2022SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learningcited by 8
  6. 2024COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modelingcited by 8
  7. 2022Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlookcited by 7
  8. 2023Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamicscited by 6
  9. 2022Computational Inference in Cognitive Science: Operational, Societal and Ethical Considerationscited by 4
  10. 2023Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosiscited by 4
  11. 2023TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Artscited by 3
  12. 2024Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Modelscited by 1
2 more
  1. 2026Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomiescited by 1
  2. 2026CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Modelscited by 0