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