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    <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.</abstract>
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%0 Conference Proceedings
%T Towards Low-Resource Real-Time Assessment of Empathy in Counselling
%A Wu, Zixiu
%A Helaoui, Rim
%A Reforgiato Recupero, Diego
%A Riboni, Daniele
%Y Goharian, Nazli
%Y Resnik, Philip
%Y Yates, Andrew
%Y Ireland, Molly
%Y Niederhoffer, Kate
%Y Resnik, Rebecca
%S Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F wu-etal-2021-towards
%X 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.
%R 10.18653/v1/2021.clpsych-1.22
%U https://aclanthology.org/2021.clpsych-1.22
%U https://doi.org/10.18653/v1/2021.clpsych-1.22
%P 204-216

Markdown (Informal)

[Towards Low-Resource Real-Time Assessment of Empathy in Counselling](https://aclanthology.org/2021.clpsych-1.22) (Wu et al., CLPsych 2021)

• Towards Low-Resource Real-Time Assessment of Empathy in Counselling (Wu et al., CLPsych 2021)

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.