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    <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.</abstract>
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%0 Conference Proceedings
%T Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
%A Cao, Jie
%A Tanana, Michael
%A Imel, Zac
%A Poitras, Eric
%A Atkins, David
%A Srikumar, Vivek
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F cao-etal-2019-observing
%X 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.
%R 10.18653/v1/P19-1563
%U https://aclanthology.org/P19-1563
%U https://doi.org/10.18653/v1/P19-1563
%P 5599-5611

Markdown (Informal)

[Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes](https://aclanthology.org/P19-1563) (Cao et al., ACL 2019)

• Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes (Cao et al., ACL 2019)

ACL

• Jie Cao, Michael Tanana, Zac Imel, Eric Poitras, David Atkins, and Vivek Srikumar. 2019. Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5599–5611, Florence, Italy. Association for Computational Linguistics.