Multi-label Multi-task Deep Learning for Behavioral Coding

TL;DR

This paper introduces a multi-label, multi-task deep learning approach to automatically code human behaviors in psychotherapy sessions, improving prediction accuracy and providing insights into complex interactions.

Contribution

It presents a novel methodology combining multi-label and multi-task learning paradigms for behavioral coding in psychotherapy, with experimental validation on real datasets.

Findings

Multi-label and multi-task learning improve behavioral prediction accuracy.

Incorporating turn context enhances model performance.

Models offer insights into therapist-client interactions.

Abstract

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.