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.
arXiv:2602. 13452v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly proposed for crisis preparedness and response, particularly for multilingual communication.
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.
arXiv:2610.32842v1 Announce Type: new Abstract: It is essential that we communicate with the entirety of an affected population during a crisis, including those who do not speak a region's dominant l...
arXiv:2607. 00019v1 Announce Type: cross Abstract: This paper offers a call to action.
arXiv:2503.22735v2 Announce Type: replace-cross Abstract: This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation tra...
arXiv:2601.02186v2 Announce Type: replace Abstract: Despite continuous advances in medical technology, the global distribution of health care resources remains uneven. The development of large langua...
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...
arXiv:2608. 19981v1 Announce Type: new Abstract: We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine.
Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapidly. We introduce UNRESTSENT200K, a Bangla crisis...
The study investigates how UK healthcare staff use Google Translate to communicate with patients who speak French or Spanish, focusing on the risks of translating medical abbreviations. Twenty-one staff members from various specialties were interviewed after reviewing translated clinical examples, and the data were analyzed qualitatively to assess awareness of potential safety hazards. The research highlights a gap in understanding how reliance on machine translation might compromise patient care, especially when dealing with abbreviated medical terminology.
arXiv:2609.16997v1 Announce Type: new Abstract: Crisis sentiment analysis is especially challenging for low-resource languages such as Bangla, where language, context, and public reaction shift rapid...
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...