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arXiv · 2604.26597

Translating Under Pressure: Domain-Aware LLMs for Crisis Communication

Antonio Castaldo·Maria Carmen Staiano·Johanna Monti·Sheila Castilho·Francesca Chiusaroli
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Abstract

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.

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Antonio Castaldo, Maria Carmen Staiano, Johanna Monti, Sheila Castilho, Francesca Chiusaroli. 2026-04-29. Translating Under Pressure: Domain-Aware LLMs for Crisis Communication. https://arxiv.org/abs/2604.26597

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