@inproceedings{tavabi-etal-2021-analysis,
title = "Analysis of Behavior Classification in Motivational Interviewing",
author = "Tavabi, Leili and
Tran, Trang and
Stefanov, Kalin and
Borsari, Brian and
Woolley, Joshua and
Scherer, Stefan and
Soleymani, Mohammad",
editor = "Goharian, Nazli and
Resnik, Philip and
Yates, Andrew and
Ireland, Molly and
Niederhoffer, Kate and
Resnik, Rebecca",
booktitle = "Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.clpsych-1.13",
doi = "10.18653/v1/2021.clpsych-1.13",
pages = "110--115",
abstract = "Analysis of client and therapist behavior in counseling sessions can provide helpful insights for assessing the quality of the session and consequently, the client{'}s behavioral outcome. In this paper, we study the automatic classification of standardized behavior codes (annotations) used for assessment of psychotherapy sessions in Motivational Interviewing (MI). We develop models and examine the classification of client behaviors throughout MI sessions, comparing the performance by models trained on large pretrained embeddings (RoBERTa) versus interpretable and expert-selected features (LIWC). Our best performing model using the pretrained RoBERTa embeddings beats the baseline model, achieving an F1 score of 0.66 in the subject-independent 3-class classification. Through statistical analysis on the classification results, we identify prominent LIWC features that may not have been captured by the model using pretrained embeddings. Although classification using LIWC features underperforms RoBERTa, our findings motivate the future direction of incorporating auxiliary tasks in the classification of MI codes.",
}
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<abstract>Analysis of client and therapist behavior in counseling sessions can provide helpful insights for assessing the quality of the session and consequently, the client’s behavioral outcome. In this paper, we study the automatic classification of standardized behavior codes (annotations) used for assessment of psychotherapy sessions in Motivational Interviewing (MI). We develop models and examine the classification of client behaviors throughout MI sessions, comparing the performance by models trained on large pretrained embeddings (RoBERTa) versus interpretable and expert-selected features (LIWC). Our best performing model using the pretrained RoBERTa embeddings beats the baseline model, achieving an F1 score of 0.66 in the subject-independent 3-class classification. Through statistical analysis on the classification results, we identify prominent LIWC features that may not have been captured by the model using pretrained embeddings. Although classification using LIWC features underperforms RoBERTa, our findings motivate the future direction of incorporating auxiliary tasks in the classification of MI codes.</abstract>
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%0 Conference Proceedings
%T Analysis of Behavior Classification in Motivational Interviewing
%A Tavabi, Leili
%A Tran, Trang
%A Stefanov, Kalin
%A Borsari, Brian
%A Woolley, Joshua
%A Scherer, Stefan
%A Soleymani, Mohammad
%Y Goharian, Nazli
%Y Resnik, Philip
%Y Yates, Andrew
%Y Ireland, Molly
%Y Niederhoffer, Kate
%Y Resnik, Rebecca
%S Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F tavabi-etal-2021-analysis
%X Analysis of client and therapist behavior in counseling sessions can provide helpful insights for assessing the quality of the session and consequently, the client’s behavioral outcome. In this paper, we study the automatic classification of standardized behavior codes (annotations) used for assessment of psychotherapy sessions in Motivational Interviewing (MI). We develop models and examine the classification of client behaviors throughout MI sessions, comparing the performance by models trained on large pretrained embeddings (RoBERTa) versus interpretable and expert-selected features (LIWC). Our best performing model using the pretrained RoBERTa embeddings beats the baseline model, achieving an F1 score of 0.66 in the subject-independent 3-class classification. Through statistical analysis on the classification results, we identify prominent LIWC features that may not have been captured by the model using pretrained embeddings. Although classification using LIWC features underperforms RoBERTa, our findings motivate the future direction of incorporating auxiliary tasks in the classification of MI codes.
%R 10.18653/v1/2021.clpsych-1.13
%U https://aclanthology.org/2021.clpsych-1.13
%U https://doi.org/10.18653/v1/2021.clpsych-1.13
%P 110-115
Markdown (Informal)
[Analysis of Behavior Classification in Motivational Interviewing](https://aclanthology.org/2021.clpsych-1.13) (Tavabi et al., CLPsych 2021)
ACL
- Leili Tavabi, Trang Tran, Kalin Stefanov, Brian Borsari, Joshua Woolley, Stefan Scherer, and Mohammad Soleymani. 2021. Analysis of Behavior Classification in Motivational Interviewing. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 110–115, Online. Association for Computational Linguistics.