# Deep Annotation of Therapeutic Working Alliance in Psychotherapy

arXiv: https://arxiv.org/abs/2204.05522 · HTML: https://mlsanity.com/paper/2204.05522/

## Key facts

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

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry](https://mlsanity.com/paper/1906.11286/) (2019)
- [Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL](https://mlsanity.com/paper/2005.04544/) (2020)
- [Predicting human decision making in psychological tasks with recurrent neural networks](https://mlsanity.com/paper/2010.11413/) (2020)

## Built on by (most cited first, 14 of 14)

- [Neural Topic Modeling of Psychotherapy Sessions](https://mlsanity.com/paper/2204.10189/) (2022)
- [Towards Healthy AI: Large Language Models Need Therapists Too](https://mlsanity.com/paper/2304.00416/) (2023)
- [Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook](https://mlsanity.com/paper/2210.13623/) (2022)
- [Working Alliance Transformer for Psychotherapy Dialogue Classification](https://mlsanity.com/paper/2210.15603/) (2022)
- [SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning](https://mlsanity.com/paper/2208.13077/) (2022)
- [COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling](https://mlsanity.com/paper/2402.14701/) (2024)
- [Knowledge Management System with NLP-Assisted Annotations: A Brief Survey and Outlook](https://mlsanity.com/paper/2206.07304/) (2022)
- [Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics](https://mlsanity.com/paper/2303.09601/) (2023)
- [Computational Inference in Cognitive Science: Operational, Societal and Ethical Considerations](https://mlsanity.com/paper/2210.13526/) (2022)
- [Utterance Classification with Logical Neural Network: Explainable AI for Mental Disorder Diagnosis](https://mlsanity.com/paper/2306.03902/) (2023)
- [TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts](https://mlsanity.com/paper/2302.10845/) (2023)
- [Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models](https://mlsanity.com/paper/2405.05060/) (2024)
- [Keyphrase Generative Representation of Youth Crisis Conversations Beyond Static Taxonomies](https://mlsanity.com/paper/2605.27546/) (2026)
- [CARE: An Explainable Computational Framework for Assessing Client-Perceived Therapeutic Alliance Using Large Language Models](https://mlsanity.com/paper/2602.20648/) (2026)

## 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.