# Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes

arXiv: https://arxiv.org/abs/1907.00326 · HTML: https://mlsanity.com/paper/1907.00326/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 25 of 25)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection](https://mlsanity.com/paper/2106.01071/) (2021)
- [Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting](https://mlsanity.com/paper/2310.07146/) (2023)
- [Understanding the Therapeutic Relationship between Counselors and Clients in Online Text-based Counseling using LLMs](https://mlsanity.com/paper/2402.11958/) (2024)
- [Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts](https://mlsanity.com/paper/2403.13786/) (2024)
- [Accounting for Sycophancy in Language Model Uncertainty Estimation](https://mlsanity.com/paper/2410.14746/) (2024)
- [Multi-dimensional Evaluation of Empathetic Dialog Responses](https://mlsanity.com/paper/2402.11409/) (2024)
- [Understanding Client Reactions in Online Mental Health Counseling](https://mlsanity.com/paper/2306.15334/) (2023)
- [Script-Strategy Aligned Generation: Aligning LLMs with Expert-Crafted Dialogue Scripts and Therapeutic Strategies for Psychotherapy](https://mlsanity.com/paper/2411.06723/) (2024)
- [Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models](https://mlsanity.com/paper/2402.03284/) (2024)
- [MAGneT: Coordinated Multi-Agent Generation of Synthetic Multi-Turn Mental Health Counseling Sessions](https://mlsanity.com/paper/2509.04183/) (2025)
- [KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy](https://mlsanity.com/paper/2502.05651/) (2025)
- [A Fully Generative Motivational Interviewing Counsellor Chatbot for Moving Smokers Towards the Decision to Quit](https://mlsanity.com/paper/2505.17362/) (2025)
- [Towards Detecting Need for Empathetic Response in Motivational Interviewing](https://mlsanity.com/paper/2105.09649/) (2021)
- [Towards Automated Real-time Evaluation in Text-based Counseling](https://mlsanity.com/paper/2203.03442/) (2022)
- [RECAP: Resistance Capture in Text-based Mental Health Counseling with Large Language Models](https://mlsanity.com/paper/2601.14780/) (2026)
- [Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models](https://mlsanity.com/paper/2402.14200/) (2024)
- [OnCoCo 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Conversations](https://mlsanity.com/paper/2512.09804/) (2025)
- [Evaluating Role-Consistency in LLMs for Counselor Training](https://mlsanity.com/paper/2601.08892/) (2026)
- [TopicDiff: A Topic-enriched Diffusion Approach for Multimodal Conversational Emotion Detection](https://mlsanity.com/paper/2403.04789/) (2024)
- [PhilHumans: Benchmarking Machine Learning for Personal Health](https://mlsanity.com/paper/2405.02770/) (2024)
- [Multi-dimensional Assessment and Explainable Feedback for Counselor Responses to Client Resistance in Text-based Counseling with LLMs](https://mlsanity.com/paper/2602.21638/) (2026)
- [Ekova: A Personality-Support Agent for Self-Discovery Dialogue](https://mlsanity.com/paper/2608.00478/) (2026)
- [Adapting from Downturns: Prediction of Long-Term Conversational-Skill Development in Mental-Health Crisis Counselors](https://mlsanity.com/paper/2609.04350/) (2026)

## Abstract (arXiv)

Automatically analyzing dialogue can help understand and guide behavior in domains such as counseling, where interactions are largely mediated by conversation. In this paper, we study modeling behavioral codes used to asses a psychotherapy treatment style called Motivational Interviewing (MI), which is effective for addressing substance abuse and related problems. Specifically, we address the problem of providing real-time guidance to therapists with a dialogue observer that (1) categorizes therapist and client MI behavioral codes and, (2) forecasts codes for upcoming utterances to help guide the conversation and potentially alert the therapist. For both tasks, we define neural network models that build upon recent successes in dialogue modeling. Our experiments demonstrate that our models can outperform several baselines for both tasks. We also report the results of a careful analysis that reveals the impact of the various network design tradeoffs for modeling therapy dialogue.