# Multi-label Multi-task Deep Learning for Behavioral Coding

arXiv: https://arxiv.org/abs/1810.12349 · HTML: https://mlsanity.com/paper/1810.12349/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts](https://mlsanity.com/paper/2403.13786/) (2024)
- [Automated Quality Assessment of Cognitive Behavioral Therapy Sessions Through Highly Contextualized Language Representations](https://mlsanity.com/paper/2102.11573/) (2021)
- [Automated Evaluation Of Psychotherapy Skills Using Speech And Language Technologies](https://mlsanity.com/paper/2102.11265/) (2021)
- [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)
- [PsyProbe: Proactive and Interpretable Dialogue through User State Modeling for Exploratory Counseling](https://mlsanity.com/paper/2601.19096/) (2026)
- [Local dynamic mode of Cognitive Behavioral Therapy](https://mlsanity.com/paper/2205.09752/) (2022)
- [Automated Utterance Labeling of Conversations Using Natural Language Processing](https://mlsanity.com/paper/2208.06525/) (2022)
- [CFiCS: Graph-Based Classification of Common Factors and Microcounseling Skills](https://mlsanity.com/paper/2503.22277/) (2025)
- [Move by Move: Measuring and Steering How LLMs Conduct Psychotherapy](https://mlsanity.com/paper/2608.21325/) (2026)

## Abstract (arXiv)

We propose a methodology for estimating human behaviors in psychotherapy sessions using mutli-label and multi-task learning paradigms. We discuss the problem of behavioral coding in which data of human interactions is the annotated with labels to describe relevant human behaviors of interest. We describe two related, yet distinct, corpora consisting of therapist client interactions in psychotherapy sessions. We experimentally compare the proposed learning approaches for estimating behaviors of interest in these datasets. Specifically, we compare single and multiple label learning approaches, single and multiple task learning approaches, and evaluate the performance of these approaches when incorporating turn context. We demonstrate the prediction performance gains which can be achieved by using the proposed paradigms and discuss the insights these models provide into these complex interactions.