Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
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DOI：
10.18653/v1/p19-1563
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发表时间：
2019-06
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影响因子：
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通讯作者：
Jie Cao;Michael J. Tanana;Zac E. Imel;E. Poitras;David C. Atkins;Vivek Srikumar
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文献类型：
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作者：
Jie Cao;Michael J. Tanana;Zac E. Imel;E. Poitras;David C. Atkins;Vivek Srikumar
自动分析对话可以帮助理解和指导咨询等领域的行为，其中互动主要是通过对话来介导的。在本文中，我们研究建模行为代码用于评估心理治疗的治疗风格称为动机访谈（MI），这是有效的解决药物滥用和相关问题。具体来说，我们解决的问题，提供实时指导治疗师与对话观察员，（1）分类治疗师和客户MI行为代码，（2）预测代码为即将到来的话语，以帮助指导对话，并可能提醒治疗师。对于这两个任务，我们定义了建立在对话建模最近成功的神经网络模型。我们的实验表明，我们的模型可以在这两个任务中超越几个基线。我们还报告了仔细分析的结果，揭示了各种网络设计权衡建模治疗对话的影响。
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.