Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions

Adam Tsakalidis, Dana Atzil-Slonim, Asaf Polakovski, Natalie Shapira, Rivka Tuval-Mashiach, Maria Liakata


Abstract
We present the first work on automatically capturing alliance rupture in transcribed therapy sessions, trained on the text and self-reported rupture scores from both therapists and clients. Our NLP baseline outperforms a strong majority baseline by a large margin and captures client reported ruptures unidentified by therapists in 40% of such cases.
Anthology ID:
2021.clpsych-1.15
Volume:
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
Month:
June
Year:
2021
Address:
Online
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
122–128
Language:
URL:
https://aclanthology.org/2021.clpsych-1.15
DOI:
10.18653/v1/2021.clpsych-1.15
Bibkey:
Cite (ACL):
Adam Tsakalidis, Dana Atzil-Slonim, Asaf Polakovski, Natalie Shapira, Rivka Tuval-Mashiach, and Maria Liakata. 2021. Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 122–128, Online. Association for Computational Linguistics.
Cite (Informal):
Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions (Tsakalidis et al., CLPsych 2021)
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PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2021.clpsych-1.15.pdf
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 2021.clpsych-1.15.OptionalSupplementaryMaterial.zip