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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.26597 |
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| _version_ | 1866914516708622336 |
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| author | Castaldo, Antonio Staiano, Maria Carmen Monti, Johanna Castilho, Sheila Chiusaroli, Francesca |
| author_facet | Castaldo, Antonio Staiano, Maria Carmen Monti, Johanna Castilho, Sheila Chiusaroli, Francesca |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26597 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Translating Under Pressure: Domain-Aware LLMs for Crisis Communication Castaldo, Antonio Staiano, Maria Carmen Monti, Johanna Castilho, Sheila Chiusaroli, Francesca Computation and Language Artificial Intelligence 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. |
| title | Translating Under Pressure: Domain-Aware LLMs for Crisis Communication |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.26597 |