Multi-Label Multi-Task Deep Learning for Behavioral Coding

We propose a methodology for estimating human behaviors in psychotherapy sessions using multi-label and multi-task learning paradigms. We discuss the problem of behavioral coding in which data of human interactions are 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 that the best multi-label, multi-task learning model with turn context achieves 18.9 and 19.5 percent absolute improvements with respect to a logistic regression classifier (for each behavioral coding task respectively) and 6.4 and 6.1 percent absolute improvements with respect to the best single-label, single-task deep neural network models. Lastly, we discuss the insights these modeling paradigms provide into these complex interactions including key commonalities and differences of behaviors within and between the two prevalent psychotherapy approaches-Motivational Interviewing and Cognitive Behavioral Therapy-considered.