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    <abstract>Communicating with the public during an emergency entails issuing warning messages promptly. In Europe, it often requires producing them in more than one language. Recent work has shown that language models can be trained to generate warning messages as a crisis unfolds, but the available data covers only English. To fill this gap, we present *CrisiLangs*, a multilingual extension of *CrisiText* (an English dataset of crisis events paired with warning messages written according to expert communication guidelines) to Italian, Bulgarian, German, and Greek. Expert native speakers translated the warning messages of 1,000 events drawn from 411 scenarios, for a total of 5,000 event-message pairs across five languages. We test the resource to fine-tune a multilingual model on all five languages jointly, with and without the emergency guidelines in the prompt, and compare it with a zero-shot baseline. The fine-tuned models outperform the baseline on ROUGE-L, BLEU and BERTScore in every language and configuration. In a human preference study, low decoding temperatures receive the most preferences in all five languages and in both configurations, while the effect of the guidelines varies by language.</abstract>
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%0 Conference Proceedings
%T CrisiLangs: A Multilingual Resource for Crisis Warning Generation
%A Gonella, Giacomo
%A Menini, Stefano
%A Guerini, Marco
%Y Ticona, Belu
%Y Xia, Fei
%Y Lewis, William D.
%Y Anastasopoulos, Antonios
%S Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-450-7
%F gonella-etal-2026-crisilangs
%X Communicating with the public during an emergency entails issuing warning messages promptly. In Europe, it often requires producing them in more than one language. Recent work has shown that language models can be trained to generate warning messages as a crisis unfolds, but the available data covers only English. To fill this gap, we present *CrisiLangs*, a multilingual extension of *CrisiText* (an English dataset of crisis events paired with warning messages written according to expert communication guidelines) to Italian, Bulgarian, German, and Greek. Expert native speakers translated the warning messages of 1,000 events drawn from 411 scenarios, for a total of 5,000 event-message pairs across five languages. We test the resource to fine-tune a multilingual model on all five languages jointly, with and without the emergency guidelines in the prompt, and compare it with a zero-shot baseline. The fine-tuned models outperform the baseline on ROUGE-L, BLEU and BERTScore in every language and configuration. In a human preference study, low decoding temperatures receive the most preferences in all five languages and in both configurations, while the effect of the guidelines varies by language.
%U https://aclanthology.org/2026.lt4cpr-1.3/
%P 20-31

Markdown (Informal)

[CrisiLangs: A Multilingual Resource for Crisis Warning Generation](https://aclanthology.org/2026.lt4cpr-1.3/) (Gonella et al., LT4CPR 2026)

• CrisiLangs: A Multilingual Resource for Crisis Warning Generation (Gonella et al., LT4CPR 2026)

ACL

• Giacomo Gonella, Stefano Menini, and Marco Guerini. 2026. CrisiLangs: A Multilingual Resource for Crisis Warning Generation. In Proceedings of the First Workshop on Language Technologies for Crisis Preparedness and Response, pages 20–31, Hengqin, China. Association for Computational Linguistics.