@inproceedings{cohen-etal-2024-motivational-interviewing,
title = "Motivational Interviewing Transcripts Annotated with Global Scores",
author = "Cohen, Ben and
Zisquit, Moreah and
Yosef, Stav and
Friedman, Doron and
Bar, Kfir",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1017",
pages = "11642--11657",
abstract = "Motivational interviewing (MI) is a counseling approach that aims to increase intrinsic motivation and commitment to change. Despite its effectiveness in various disorders such as addiction, weight loss, and smoking cessation, publicly available annotated MI datasets are scarce, limiting the development and evaluation of MI language generation models. We present MI-TAGS, a new annotated dataset of MI therapy sessions written in English collected from video recordings available on public sources. The dataset includes 242 MI demonstration transcripts annotated with the MI Treatment Integrity (MITI) 4.2 therapist behavioral codes and global scores, and Client Language EAsy Rating (CLEAR) 1.0 tags for client speech. In this paper we describe the process of data collection, transcription, and annotation, and provide an analysis of the new dataset. Additionally, we explore the potential use of the dataset for training language models to perform several MITI classification tasks; our results suggest that models may be able to automatically provide utterance-level annotation as well as global scores, with performance comparable to human annotators.",
}
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%0 Conference Proceedings
%T Motivational Interviewing Transcripts Annotated with Global Scores
%A Cohen, Ben
%A Zisquit, Moreah
%A Yosef, Stav
%A Friedman, Doron
%A Bar, Kfir
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F cohen-etal-2024-motivational-interviewing
%X Motivational interviewing (MI) is a counseling approach that aims to increase intrinsic motivation and commitment to change. Despite its effectiveness in various disorders such as addiction, weight loss, and smoking cessation, publicly available annotated MI datasets are scarce, limiting the development and evaluation of MI language generation models. We present MI-TAGS, a new annotated dataset of MI therapy sessions written in English collected from video recordings available on public sources. The dataset includes 242 MI demonstration transcripts annotated with the MI Treatment Integrity (MITI) 4.2 therapist behavioral codes and global scores, and Client Language EAsy Rating (CLEAR) 1.0 tags for client speech. In this paper we describe the process of data collection, transcription, and annotation, and provide an analysis of the new dataset. Additionally, we explore the potential use of the dataset for training language models to perform several MITI classification tasks; our results suggest that models may be able to automatically provide utterance-level annotation as well as global scores, with performance comparable to human annotators.
%U https://aclanthology.org/2024.lrec-main.1017
%P 11642-11657
Markdown (Informal)
[Motivational Interviewing Transcripts Annotated with Global Scores](https://aclanthology.org/2024.lrec-main.1017) (Cohen et al., LREC-COLING 2024)
ACL
- Ben Cohen, Moreah Zisquit, Stav Yosef, Doron Friedman, and Kfir Bar. 2024. Motivational Interviewing Transcripts Annotated with Global Scores. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11642–11657, Torino, Italia. ELRA and ICCL.