@inproceedings{le-van-kiem-etal-2026-crisiscl,
    title = "{C}risis{CL}: A Domain Incremental Learning Benchmark for Crisis Management",
    author = "Le Van Kiem, Paul  and
      Meunier, Romain  and
      Benamara, Farah  and
      MORICEAU, V{\'e}ronique",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-1.850/",
    doi = "10.63317/5eem8gu9j9o8",
    pages = "10853--10865",
    abstract = "This paper proposes CrisisCL, a domain incremental learning benchmark for crisis management. Based on previous crisis management protocols, it improves consistency by allowing continual learning (CL) of new crises. A set of experiments have been conducted on multilingual datasets relying on continual learning methods and transformers to improve performance and ensure model generalization. Results reveal that regularization methods are more effective on large, coherent domains, whereas replay strategies struggle under constrained memory. Additional experimental protocols further expose the limitations of current CL methods when generalizing to unforeseen crisis events."
}