@inproceedings{fataliyev-etal-2023-predicting,
title = "Predicting Empathic Accuracy from User-Designer Interviews",
author = "Fataliyev, Steven Nguyen and
Beck, Daniel and
Holtta-Otto, Katja",
editor = "Muresan, Smaranda and
Chen, Vivian and
Casey, Kennington and
David, Vandyke and
Nina, Dethlefs and
Koji, Inoue and
Erik, Ekstedt and
Stefan, Ultes",
booktitle = "Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association",
month = nov,
year = "2023",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.alta-1.14",
pages = "125--129",
abstract = "Measuring empathy as a natural language processing task has often been limited to a subjective measure of how well individuals respond to each other in emotive situations. Cognitive empathy, or an individual{'}s ability to accurately assess another individual{'}s thoughts, remains a more novel task. In this paper, we explore natural language processing techniques to measure cognitive empathy using paired sentence data from design interviews. Our findings show that an unsupervised approach based on similarity of vectors from a Large Language Model is surprisingly promising, while adding supervision does not necessarily improve the performance. An analysis of the results highlights potential reasons for this behaviour and gives directions for future work in this space.",
}
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<abstract>Measuring empathy as a natural language processing task has often been limited to a subjective measure of how well individuals respond to each other in emotive situations. Cognitive empathy, or an individual’s ability to accurately assess another individual’s thoughts, remains a more novel task. In this paper, we explore natural language processing techniques to measure cognitive empathy using paired sentence data from design interviews. Our findings show that an unsupervised approach based on similarity of vectors from a Large Language Model is surprisingly promising, while adding supervision does not necessarily improve the performance. An analysis of the results highlights potential reasons for this behaviour and gives directions for future work in this space.</abstract>
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%0 Conference Proceedings
%T Predicting Empathic Accuracy from User-Designer Interviews
%A Fataliyev, Steven Nguyen
%A Beck, Daniel
%A Holtta-Otto, Katja
%Y Muresan, Smaranda
%Y Chen, Vivian
%Y Casey, Kennington
%Y David, Vandyke
%Y Nina, Dethlefs
%Y Koji, Inoue
%Y Erik, Ekstedt
%Y Stefan, Ultes
%S Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association
%D 2023
%8 November
%I Association for Computational Linguistics
%C Melbourne, Australia
%F fataliyev-etal-2023-predicting
%X Measuring empathy as a natural language processing task has often been limited to a subjective measure of how well individuals respond to each other in emotive situations. Cognitive empathy, or an individual’s ability to accurately assess another individual’s thoughts, remains a more novel task. In this paper, we explore natural language processing techniques to measure cognitive empathy using paired sentence data from design interviews. Our findings show that an unsupervised approach based on similarity of vectors from a Large Language Model is surprisingly promising, while adding supervision does not necessarily improve the performance. An analysis of the results highlights potential reasons for this behaviour and gives directions for future work in this space.
%U https://aclanthology.org/2023.alta-1.14
%P 125-129
Markdown (Informal)
[Predicting Empathic Accuracy from User-Designer Interviews](https://aclanthology.org/2023.alta-1.14) (Fataliyev et al., ALTA 2023)
ACL
- Steven Nguyen Fataliyev, Daniel Beck, and Katja Holtta-Otto. 2023. Predicting Empathic Accuracy from User-Designer Interviews. In Proceedings of the 21st Annual Workshop of the Australasian Language Technology Association, pages 125–129, Melbourne, Australia. Association for Computational Linguistics.