Michael Tanana


2019

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Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
Jie Cao | Michael Tanana | Zac Imel | Eric Poitras | David Atkins | Vivek Srikumar
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

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.

2016

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Is Sentiment in Movies the Same as Sentiment in Psychotherapy? Comparisons Using a New Psychotherapy Sentiment Database
Michael Tanana | Aaron Dembe | Christina S. Soma | Zac Imel | David Atkins | Vivek Srikumar
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

2015

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Recursive Neural Networks for Coding Therapist and Patient Behavior in Motivational Interviewing
Michael Tanana | Kevin Hallgren | Zac Imel | David Atkins | Padhraic Smyth | Vivek Srikumar
Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality