@inproceedings{numanoglu-etal-2024-empathify,
title = "Empathify at {WASSA} 2024 Empathy and Personality Shared Task: Contextualizing Empathy with a {BERT}-Based Context-Aware Approach for Empathy Detection",
author = {Numano{\u{g}}lu, Arda and
Ate{\c{s}}, S{\"u}leyman and
Cicekli, Nihan and
K{\"u}{\c{c}}{\"u}k, Dilek},
editor = "De Clercq, Orph{\'e}e and
Barriere, Valentin and
Barnes, Jeremy and
Klinger, Roman and
Sedoc, Jo{\~a}o and
Tafreshi, Shabnam",
booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wassa-1.33",
doi = "10.18653/v1/2024.wassa-1.33",
pages = "393--398",
abstract = "Empathy detection from textual data is a complex task that requires an understanding of both the content and context of the text. This study presents a BERT-based context-aware approach to enhance empathy detection in conversations and essays. We participated in the WASSA 2024 Shared Task, focusing on two tracks: empathy and emotion prediction in conversations (CONV-turn) and empathy and distress prediction in essays (EMP). Our approach leverages contextual information by incorporating related articles and emotional characteristics as additional inputs, using BERT-based Siamese (parallel) architecture. Our experiments demonstrated that using article summaries as context significantly improves performance, with the parallel BERT approach outperforming the traditional method of concatenating inputs with the {`}[SEP]{`} token. These findings highlight the importance of context-awareness in empathy detection and pave the way for future improvements in the sensitivity and accuracy of such systems.",
}
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%0 Conference Proceedings
%T Empathify at WASSA 2024 Empathy and Personality Shared Task: Contextualizing Empathy with a BERT-Based Context-Aware Approach for Empathy Detection
%A Numanoğlu, Arda
%A Ateş, Süleyman
%A Cicekli, Nihan
%A Küçük, Dilek
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F numanoglu-etal-2024-empathify
%X Empathy detection from textual data is a complex task that requires an understanding of both the content and context of the text. This study presents a BERT-based context-aware approach to enhance empathy detection in conversations and essays. We participated in the WASSA 2024 Shared Task, focusing on two tracks: empathy and emotion prediction in conversations (CONV-turn) and empathy and distress prediction in essays (EMP). Our approach leverages contextual information by incorporating related articles and emotional characteristics as additional inputs, using BERT-based Siamese (parallel) architecture. Our experiments demonstrated that using article summaries as context significantly improves performance, with the parallel BERT approach outperforming the traditional method of concatenating inputs with the ‘[SEP]‘ token. These findings highlight the importance of context-awareness in empathy detection and pave the way for future improvements in the sensitivity and accuracy of such systems.
%R 10.18653/v1/2024.wassa-1.33
%U https://aclanthology.org/2024.wassa-1.33
%U https://doi.org/10.18653/v1/2024.wassa-1.33
%P 393-398
Markdown (Informal)
[Empathify at WASSA 2024 Empathy and Personality Shared Task: Contextualizing Empathy with a BERT-Based Context-Aware Approach for Empathy Detection](https://aclanthology.org/2024.wassa-1.33) (Numanoğlu et al., WASSA-WS 2024)
ACL