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arXiv:2602. 13452v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly proposed for crisis preparedness and response, particularly for multilingual communication.
arXiv:2607. 00019v1 Announce Type: cross Abstract: This paper offers a call to action.
arXiv:2610.08601v1 Announce Type: new Abstract: Emergency messaging such as extreme-weather reports and earthquake instructions can involve high stakes, to the extent that translation errors can lead...
CrisiLangs is a multilingual extension of the CrisiText dataset, adding Italian, Bulgarian, German, and Greek translations of warning messages for 1,000 crisis events, resulting in 5,000 event‑message pairs across five languages. The resource was used to fine‑tune a multilingual model, which outperformed a zero‑shot baseline on ROUGE‑L, BLEU, and BERTScore in every language and configuration. Human preference studies showed that low decoding temperatures were favored across all languages, while the impact of emergency guidelines varied by language.
The article investigates whether commercial machine translation (MT) tools—Google Translate, Microsoft Translator, GPT, Gemini, and Claude—are ready to support communication during crises or pandemics. Using the TICO-19 dataset, which contains pandemic-related sentences in high-priority low-resource languages, the study evaluates the usability of translations produced by these systems. The goal is to determine how effectively these MT tools can bridge language gaps between governments, aid providers, medical professionals, and affected communities.
Emergency messaging such as extreme-weather reports and earthquake instructions can involve high stakes, to the extent that translation errors can lead to tragic consequences. The use of machine trans...