Towards Low-Resource Real-Time Assessment of Empathy in Counselling

Zixiu Wu, Rim Helaoui, Diego Reforgiato Recupero, Daniele Riboni


Abstract
Gauging therapist empathy in counselling is an important component of understanding counselling quality. While session-level empathy assessment based on machine learning has been investigated extensively, it relies on relatively large amounts of well-annotated dialogue data, and real-time evaluation has been overlooked in the past. In this paper, we focus on the task of low-resource utterance-level binary empathy assessment. We train deep learning models on heuristically constructed empathy vs. non-empathy contrast in general conversations, and apply the models directly to therapeutic dialogues, assuming correlation between empathy manifested in those two domains. We show that such training yields poor performance in general, probe its causes, and examine the actual effect of learning from empathy contrast in general conversation.
Anthology ID:
2021.clpsych-1.22
Volume:
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
Month:
June
Year:
2021
Address:
Online
Editors:
Nazli Goharian, Philip Resnik, Andrew Yates, Molly Ireland, Kate Niederhoffer, Rebecca Resnik
Venue:
CLPsych
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
204–216
Language:
URL:
https://aclanthology.org/2021.clpsych-1.22
DOI:
10.18653/v1/2021.clpsych-1.22
Bibkey:
Cite (ACL):
Zixiu Wu, Rim Helaoui, Diego Reforgiato Recupero, and Daniele Riboni. 2021. Towards Low-Resource Real-Time Assessment of Empathy in Counselling. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 204–216, Online. Association for Computational Linguistics.
Cite (Informal):
Towards Low-Resource Real-Time Assessment of Empathy in Counselling (Wu et al., CLPsych 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.clpsych-1.22.pdf
Data
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