Translating Under Pressure: Domain-Aware LLMs for Crisis Communication
Antonio Castaldo, Maria Carmen Staiano, Johanna Monti, Sheila Castilho, Francesca Chiusaroli
Correct Metadata for
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.- Anthology ID:
- 2026.eamt-1.5
- Volume:
- Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
- Month:
- June
- Year:
- 2026
- Address:
- Tilburg, The Netherlands
- Editors:
- Dimitar Shterionov, Eva Vanmassenhove, Mirella De Sisto, Fred Blain, Javad Pourmostafa Roshan Sharami, Lisa Lepp, Chiara Manna, Argentina Anna Rescigno, Alina Karakanta, Ayla Rigouts Terryn, Manuel Lardelli, Natalia Resende, Elena Murgolo, Janiça Hackenbuchner, Anna Zaretskaya, Miquel Esplà-Gomis, Thierry Etchegoyhen, Dagmar Gromann, Rachel Bawden, Barry Haddow, Sara Szoc, Mikel Forcada, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 48–60
- Language:
- URL:
- https://aclanthology.org/2026.eamt-1.5/
- DOI:
- Bibkey:
- Cite (ACL):
- Antonio Castaldo, Maria Carmen Staiano, Johanna Monti, Sheila Castilho, and Francesca Chiusaroli. 2026. Translating Under Pressure: Domain-Aware LLMs for Crisis Communication. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 48–60, Tilburg, The Netherlands. European Association for Machine Translation.
- Cite (Informal):
- Translating Under Pressure: Domain-Aware LLMs for Crisis Communication (Castaldo et al., EAMT 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.eamt-1.5.pdf
Export citation
@inproceedings{castaldo-etal-2026-translating,
title = "Translating Under Pressure: Domain-Aware {LLM}s for Crisis Communication",
author = "Castaldo, Antonio and
Staiano, Maria Carmen and
Monti, Johanna and
Castilho, Sheila and
Chiusaroli, Francesca",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.5/",
pages = "48--60",
ISBN = "9789403901411",
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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%0 Conference Proceedings %T Translating Under Pressure: Domain-Aware LLMs for Crisis Communication %A Castaldo, Antonio %A Staiano, Maria Carmen %A Monti, Johanna %A Castilho, Sheila %A Chiusaroli, Francesca %Y Shterionov, Dimitar %Y Vanmassenhove, Eva %Y De Sisto, Mirella %Y Blain, Fred %Y Pourmostafa Roshan Sharami, Javad %Y Lepp, Lisa %Y Manna, Chiara %Y Rescigno, Argentina Anna %Y Karakanta, Alina %Y Rigouts Terryn, Ayla %Y Lardelli, Manuel %Y Resende, Natalia %Y Murgolo, Elena %Y Hackenbuchner, Janiça %Y Zaretskaya, Anna %Y Esplà-Gomis, Miquel %Y Etchegoyhen, Thierry %Y Gromann, Dagmar %Y Bawden, Rachel %Y Haddow, Barry %Y Szoc, Sara %Y Forcada, Mikel %Y Moniz, Helena %S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1) %D 2026 %8 June %I European Association for Machine Translation %C Tilburg, The Netherlands %@ 9789403901411 %F castaldo-etal-2026-translating %X 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. %U https://aclanthology.org/2026.eamt-1.5/ %P 48-60
Markdown (Informal)
[Translating Under Pressure: Domain-Aware LLMs for Crisis Communication](https://aclanthology.org/2026.eamt-1.5/) (Castaldo et al., EAMT 2026)
- Translating Under Pressure: Domain-Aware LLMs for Crisis Communication (Castaldo et al., EAMT 2026)
ACL
- Antonio Castaldo, Maria Carmen Staiano, Johanna Monti, Sheila Castilho, and Francesca Chiusaroli. 2026. Translating Under Pressure: Domain-Aware LLMs for Crisis Communication. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 48–60, Tilburg, The Netherlands. European Association for Machine Translation.