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...
arXiv:2608. 14651v1 Announce Type: new Abstract: Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individuals with access and functional needs, including hard-of-hearing individuals, pregnant women, mothers with toddlers, and elderly individuals with dementia.
The paper introduces BEACON, a multilingual agent system designed to deliver personalized wildfire evacuation guidance to users, including navigation routes, checklists, and a chatbot that adapts to the user’s language. It integrates real‑time fire perimeter, evacuation orders, shelter data, GPS, and NOAA weather to predict fire danger using an XGBoost model and triggers alerts with polygon‑avoidant routing when danger is likely. The interface automatically switches languages based on the user’s recent settings or chatbot interactions, ensuring inclusive communication for non‑English speakers.
arXiv:2601.02933v4 Announce Type: replace Abstract: Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics because it is n...
Effective crisis response requires spatially grounded communication that bridges linguistic guidance of civilians with the physical environment, accounting for structural bottlenecks, evolving threats, and agent-specific contexts. Yet, current NLP research in crisis communication remains mainly limited to static, text-only classification settings, overlooking the critical communicative role of AI operators in dynamic, embodied scenarios.
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:2606. 27106v1 Announce Type: cross Abstract: We present a novel approach for applying Large Language Models (LLMs) to threat assessment in the context of foreign peacekeeping missions.