Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2019
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| _version_ | 1866912517417074688 |
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| author | Cao, Jie Tanana, Michael Imel, Zac E. Poitras, Eric Atkins, David C. Srikumar, Vivek |
| author_facet | Cao, Jie Tanana, Michael Imel, Zac E. Poitras, Eric Atkins, David C. Srikumar, Vivek |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1907_00326 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes Cao, Jie Tanana, Michael Imel, Zac E. Poitras, Eric Atkins, David C. Srikumar, Vivek Computation and Language Artificial Intelligence Human-Computer Interaction Machine Learning 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. |
| title | Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/1907.00326 |