ACL 2019.
Abstract
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.
Links
Bib Entry
@inproceedings{cao2019observing, author = {Cao, Jie and Tanana, Michael and Imel, Zac E. and Poitras, Eric and Atkins, David C and Srikumar, Vivek}, title = {Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes}, booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics}, year = {2019} }