Multi-label Multi-task Deep Learning for Behavioral Coding
This work addresses behavioral coding in psychotherapy, an incremental improvement for computational analysis of human interactions.
The researchers tackled the problem of estimating human behaviors in psychotherapy sessions by proposing multi-label and multi-task deep learning approaches, demonstrating performance gains through experimental comparisons on two therapist-client interaction datasets.
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.