@inproceedings{lee-etal-2019-identifying,
title = "Identifying therapist conversational actions across diverse psychotherapeutic approaches",
author = "Lee, Fei-Tzin and
Hull, Derrick and
Levine, Jacob and
Ray, Bonnie and
McKeown, Kathy",
editor = "Niederhoffer, Kate and
Hollingshead, Kristy and
Resnik, Philip and
Resnik, Rebecca and
Loveys, Kate",
booktitle = "Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-3002",
doi = "10.18653/v1/W19-3002",
pages = "12--23",
abstract = "While conversation in therapy sessions can vary widely in both topic and style, an understanding of the underlying techniques used by therapists can provide valuable insights into how therapists best help clients of different types. Dialogue act classification aims to identify the conversational {``}action{''} each speaker takes at each utterance, such as sympathizing, problem-solving or assumption checking. We propose to apply dialogue act classification to therapy transcripts, using a therapy-specific labeling scheme, in order to gain a high-level understanding of the flow of conversation in therapy sessions. We present a novel annotation scheme that spans multiple psychotherapeutic approaches, apply it to a large and diverse corpus of psychotherapy transcripts, and present and discuss classification results obtained using both SVM and neural network-based models. The results indicate that identifying the structure and flow of therapeutic actions is an obtainable goal, opening up the opportunity in the future to provide therapeutic recommendations tailored to specific client situations.",
}
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<abstract>While conversation in therapy sessions can vary widely in both topic and style, an understanding of the underlying techniques used by therapists can provide valuable insights into how therapists best help clients of different types. Dialogue act classification aims to identify the conversational “action” each speaker takes at each utterance, such as sympathizing, problem-solving or assumption checking. We propose to apply dialogue act classification to therapy transcripts, using a therapy-specific labeling scheme, in order to gain a high-level understanding of the flow of conversation in therapy sessions. We present a novel annotation scheme that spans multiple psychotherapeutic approaches, apply it to a large and diverse corpus of psychotherapy transcripts, and present and discuss classification results obtained using both SVM and neural network-based models. The results indicate that identifying the structure and flow of therapeutic actions is an obtainable goal, opening up the opportunity in the future to provide therapeutic recommendations tailored to specific client situations.</abstract>
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%0 Conference Proceedings
%T Identifying therapist conversational actions across diverse psychotherapeutic approaches
%A Lee, Fei-Tzin
%A Hull, Derrick
%A Levine, Jacob
%A Ray, Bonnie
%A McKeown, Kathy
%Y Niederhoffer, Kate
%Y Hollingshead, Kristy
%Y Resnik, Philip
%Y Resnik, Rebecca
%Y Loveys, Kate
%S Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F lee-etal-2019-identifying
%X While conversation in therapy sessions can vary widely in both topic and style, an understanding of the underlying techniques used by therapists can provide valuable insights into how therapists best help clients of different types. Dialogue act classification aims to identify the conversational “action” each speaker takes at each utterance, such as sympathizing, problem-solving or assumption checking. We propose to apply dialogue act classification to therapy transcripts, using a therapy-specific labeling scheme, in order to gain a high-level understanding of the flow of conversation in therapy sessions. We present a novel annotation scheme that spans multiple psychotherapeutic approaches, apply it to a large and diverse corpus of psychotherapy transcripts, and present and discuss classification results obtained using both SVM and neural network-based models. The results indicate that identifying the structure and flow of therapeutic actions is an obtainable goal, opening up the opportunity in the future to provide therapeutic recommendations tailored to specific client situations.
%R 10.18653/v1/W19-3002
%U https://aclanthology.org/W19-3002
%U https://doi.org/10.18653/v1/W19-3002
%P 12-23
Markdown (Informal)
[Identifying therapist conversational actions across diverse psychotherapeutic approaches](https://aclanthology.org/W19-3002) (Lee et al., CLPsych 2019)
ACL