@inproceedings{furniturewala-jaidka-2024-empaths,
title = "Empaths at {WASSA} 2024 Empathy and Personality Shared Task: Turn-Level Empathy Prediction Using Psychological Indicators",
author = "Furniturewala, Shaz and
Jaidka, Kokil",
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-1.35/",
doi = "10.18653/v1/2024.wassa-1.35",
pages = "404--411",
abstract = "For the WASSA 2024 Empathy and Personality Prediction Shared Task, we propose a novel turn-level empathy detection method that decomposes empathy into six psychological indicators: Emotional Language, Perspective-Taking, Sympathy and Compassion, Extroversion, Openness, and Agreeableness. A pipeline of text enrichment using a Large Language Model (LLM) followed by DeBERTA fine-tuning demonstrates a significant improvement in the Pearson Correlation Coefficient and F1 scores for empathy detection, highlighting the effectiveness of our approach. Our system officially ranked 7th at the CONV-turn track."
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%0 Conference Proceedings
%T Empaths at WASSA 2024 Empathy and Personality Shared Task: Turn-Level Empathy Prediction Using Psychological Indicators
%A Furniturewala, Shaz
%A Jaidka, Kokil
%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 furniturewala-jaidka-2024-empaths
%X For the WASSA 2024 Empathy and Personality Prediction Shared Task, we propose a novel turn-level empathy detection method that decomposes empathy into six psychological indicators: Emotional Language, Perspective-Taking, Sympathy and Compassion, Extroversion, Openness, and Agreeableness. A pipeline of text enrichment using a Large Language Model (LLM) followed by DeBERTA fine-tuning demonstrates a significant improvement in the Pearson Correlation Coefficient and F1 scores for empathy detection, highlighting the effectiveness of our approach. Our system officially ranked 7th at the CONV-turn track.
%R 10.18653/v1/2024.wassa-1.35
%U https://aclanthology.org/2024.wassa-1.35/
%U https://doi.org/10.18653/v1/2024.wassa-1.35
%P 404-411
Markdown (Informal)
[Empaths at WASSA 2024 Empathy and Personality Shared Task: Turn-Level Empathy Prediction Using Psychological Indicators](https://aclanthology.org/2024.wassa-1.35/) (Furniturewala & Jaidka, WASSA 2024)
ACL