CrisisCL: A Domain Incremental Learning Benchmark for Crisis Management

Paul Le Van Kiem; Romain Meunier; Farah Benamara; Véronique MORICEAU

doi:10.63317/5eem8gu9j9o8

CrisisCL: A Domain Incremental Learning Benchmark for Crisis Management

Paul Le Van Kiem, Romain Meunier, Farah Benamara, Véronique MORICEAU

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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.

Anthology ID:: 2026.lrec-1.850
Volume:: Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:: LREC
SIG:
Publisher:: ELRA Language Resource Association
Note:
Pages:: 10853–10865
Language:
External URL:: https://lrec.elra.info/lrec2026-main-850
DOI:: 10.63317/5eem8gu9j9o8
Bibkey:
Cite (ACL):: Paul Le Van Kiem, Romain Meunier, Farah Benamara, and Véronique MORICEAU. 2026. CrisisCL: A Domain Incremental Learning Benchmark for Crisis Management. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 10853–10865, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):: CrisisCL: A Domain Incremental Learning Benchmark for Crisis Management (Le Van Kiem et al., LREC 2026)
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@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://aclanthology.org/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."
}

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%0 Conference Proceedings
%T CrisisCL: A Domain Incremental Learning Benchmark for Crisis Management
%A Le Van Kiem, Paul
%A Meunier, Romain
%A Benamara, Farah
%A MORICEAU, Véronique
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F le-van-kiem-etal-2026-crisiscl
%X 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.
%R 10.63317/5eem8gu9j9o8
%U https://aclanthology.org/2026.lrec-1.850/
%U https://doi.org/10.63317/5eem8gu9j9o8
%P 10853-10865

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Markdown (Informal)

[CrisisCL: A Domain Incremental Learning Benchmark for Crisis Management](https://aclanthology.org/2026.lrec-1.850/) (Le Van Kiem et al., LREC 2026)

CrisisCL: A Domain Incremental Learning Benchmark for Crisis Management (Le Van Kiem et al., LREC 2026)

ACL

Paul Le Van Kiem, Romain Meunier, Farah Benamara, and Véronique MORICEAU. 2026. CrisisCL: A Domain Incremental Learning Benchmark for Crisis Management. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 10853–10865, Palma de Mallorca, Spain. ELRA Language Resource Association.