Timely and reliable multilingual communication is critical during natural and human-induced disasters, but developing effective solutions for crisis communication is limited by the scarcity of curated parallel data. We propose a domain-adaptive pipeline that expands a small reference corpus, by retrieving and filtering data from general corpora. We use the resulting dataset to fine-tune a small language model for crisis-domain translation and then apply preference optimization to bias outputs toward CEFR A2-level English. Automatic and human evaluation shows that this approach improves readability, while maintaining strong adequacy. Our results indicate that simplified English, combined with domain adaptation, can function as a practical lingua franca for emergency communication when full multilingual coverage is not feasible.
@article{arxiv.2604.26597,
title = {Translating Under Pressure: Domain-Aware LLMs for Crisis Communication},
author = {Antonio Castaldo and Maria Carmen Staiano and Johanna Monti and Sheila Castilho and Francesca Chiusaroli},
journal= {arXiv preprint arXiv:2604.26597},
year = {2026}
}