@inproceedings{dina-etal-2025-edar,
title = "{EDAR}: A pipeline for Emotion and Dialogue Act Recognition",
author = "Dina, Elie and
Ayachi Kibech, Rania and
Couceiro, Miguel",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.15/",
pages = "175--186",
abstract = "Individuals facing financial difficulties often make decisions driven by emotions rather than rational analysis. EDAR, a pipeline for Emotion and Dialogue Act Recognition, is designed specifically for the debt collection process in France. By integrating EDAR into decision-making systems, debt collection outcomes could be improved. The pipeline employs Machine Learning and Deep Learning models, demonstrating that smaller models with fewer parameters can achieve high performance, offering an efficient alternative to large language models."
}
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<abstract>Individuals facing financial difficulties often make decisions driven by emotions rather than rational analysis. EDAR, a pipeline for Emotion and Dialogue Act Recognition, is designed specifically for the debt collection process in France. By integrating EDAR into decision-making systems, debt collection outcomes could be improved. The pipeline employs Machine Learning and Deep Learning models, demonstrating that smaller models with fewer parameters can achieve high performance, offering an efficient alternative to large language models.</abstract>
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%0 Conference Proceedings
%T EDAR: A pipeline for Emotion and Dialogue Act Recognition
%A Dina, Elie
%A Ayachi Kibech, Rania
%A Couceiro, Miguel
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%Y Darwish, Kareem
%Y Agarwal, Apoorv
%S Proceedings of the 31st International Conference on Computational Linguistics: Industry Track
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F dina-etal-2025-edar
%X Individuals facing financial difficulties often make decisions driven by emotions rather than rational analysis. EDAR, a pipeline for Emotion and Dialogue Act Recognition, is designed specifically for the debt collection process in France. By integrating EDAR into decision-making systems, debt collection outcomes could be improved. The pipeline employs Machine Learning and Deep Learning models, demonstrating that smaller models with fewer parameters can achieve high performance, offering an efficient alternative to large language models.
%U https://aclanthology.org/2025.coling-industry.15/
%P 175-186
Markdown (Informal)
[EDAR: A pipeline for Emotion and Dialogue Act Recognition](https://aclanthology.org/2025.coling-industry.15/) (Dina et al., COLING 2025)
ACL
- Elie Dina, Rania Ayachi Kibech, and Miguel Couceiro. 2025. EDAR: A pipeline for Emotion and Dialogue Act Recognition. In Proceedings of the 31st International Conference on Computational Linguistics: Industry Track, pages 175–186, Abu Dhabi, UAE. Association for Computational Linguistics.