Understanding and Predicting Empathic Behavior in Counseling Therapy

Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An


Abstract
Counselor empathy is associated with better outcomes in psychology and behavioral counseling. In this paper, we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy during motivational interviewing encounters. Particularly, we analyze aspects such as participants’ engagement, participants’ verbal and nonverbal accommodation, as well as topics being discussed during the conversation, with the final goal of identifying linguistic and acoustic markers of counselor empathy. We also show how we can use these findings alongside other raw linguistic and acoustic features to build accurate counselor empathy classifiers with accuracies of up to 80%.
Anthology ID:
P17-1131
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1426–1435
Language:
URL:
https://aclanthology.org/P17-1131
DOI:
10.18653/v1/P17-1131
Bibkey:
Cite (ACL):
Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, and Lawrence An. 2017. Understanding and Predicting Empathic Behavior in Counseling Therapy. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1426–1435, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Understanding and Predicting Empathic Behavior in Counseling Therapy (Pérez-Rosas et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1131.pdf