@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}