@inproceedings{briva-iglesias-fernandez-2026-ai,
title = "{AI}-assisted cultural heritage dissemination: Comparing {NMT} and glossary-augmented {LLM} translation in rock art documents",
author = "Briva-Iglesias, Vicent and
Fern{\'a}ndez, Mar{\'i}a Ferre",
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-1.49/",
pages = "774--786",
ISBN = "9789403901411",
abstract = "Cultural heritage institutions increasingly disseminate research and interpretive materials globally, but multilingual dissemination is constrained by limited budgets and staffing. In terminology-dense domains such as rock art, translation quality depends on accurate, consistent specialised terms, and small lexical errors can mislead non-specialists and reduce reuse. We compare three English MT setups for a Spanish academic rock art text: (1) DeepL as a strong NMT baseline, (2) Gemini-Simple (LLM with a basic prompt), and (3) Gemini-RAG (the same LLM with glossary-augmented prompting via lightweight retrieval of term pairs). Using PEARMUT, we conduct a human evaluation via (i) multi-way Direct Assessment (0{--}100) on 91 segments (1,743 Spanish words) and (ii) targeted terminology auditing with a restricted MQM taxonomy. Gemini-RAG yields the highest exact-match terminology accuracy (81.4{\%}), versus Gemini-Simple (69.1{\%}) and DeepL (64.4{\%}), while preserving overall quality (mean DA 85.3 Gemini-RAG vs. 85.2 Gemini-Simple) and outperforming DeepL (80.3). These results show that glossary-augmented prompting is a low-overhead way to improve terminology control in cultural-heritage translation, provided that institutions maintain minimal terminology resources and lightweight evaluation procedures."
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<namePart type="given">Argentina</namePart>
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<namePart type="given">Alina</namePart>
<namePart type="family">Karakanta</namePart>
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<namePart type="given">Ayla</namePart>
<namePart type="family">Rigouts Terryn</namePart>
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<namePart type="given">Manuel</namePart>
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<abstract>Cultural heritage institutions increasingly disseminate research and interpretive materials globally, but multilingual dissemination is constrained by limited budgets and staffing. In terminology-dense domains such as rock art, translation quality depends on accurate, consistent specialised terms, and small lexical errors can mislead non-specialists and reduce reuse. We compare three English MT setups for a Spanish academic rock art text: (1) DeepL as a strong NMT baseline, (2) Gemini-Simple (LLM with a basic prompt), and (3) Gemini-RAG (the same LLM with glossary-augmented prompting via lightweight retrieval of term pairs). Using PEARMUT, we conduct a human evaluation via (i) multi-way Direct Assessment (0–100) on 91 segments (1,743 Spanish words) and (ii) targeted terminology auditing with a restricted MQM taxonomy. Gemini-RAG yields the highest exact-match terminology accuracy (81.4%), versus Gemini-Simple (69.1%) and DeepL (64.4%), while preserving overall quality (mean DA 85.3 Gemini-RAG vs. 85.2 Gemini-Simple) and outperforming DeepL (80.3). These results show that glossary-augmented prompting is a low-overhead way to improve terminology control in cultural-heritage translation, provided that institutions maintain minimal terminology resources and lightweight evaluation procedures.</abstract>
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%0 Conference Proceedings
%T AI-assisted cultural heritage dissemination: Comparing NMT and glossary-augmented LLM translation in rock art documents
%A Briva-Iglesias, Vicent
%A Fernández, María Ferre
%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 briva-iglesias-fernandez-2026-ai
%X Cultural heritage institutions increasingly disseminate research and interpretive materials globally, but multilingual dissemination is constrained by limited budgets and staffing. In terminology-dense domains such as rock art, translation quality depends on accurate, consistent specialised terms, and small lexical errors can mislead non-specialists and reduce reuse. We compare three English MT setups for a Spanish academic rock art text: (1) DeepL as a strong NMT baseline, (2) Gemini-Simple (LLM with a basic prompt), and (3) Gemini-RAG (the same LLM with glossary-augmented prompting via lightweight retrieval of term pairs). Using PEARMUT, we conduct a human evaluation via (i) multi-way Direct Assessment (0–100) on 91 segments (1,743 Spanish words) and (ii) targeted terminology auditing with a restricted MQM taxonomy. Gemini-RAG yields the highest exact-match terminology accuracy (81.4%), versus Gemini-Simple (69.1%) and DeepL (64.4%), while preserving overall quality (mean DA 85.3 Gemini-RAG vs. 85.2 Gemini-Simple) and outperforming DeepL (80.3). These results show that glossary-augmented prompting is a low-overhead way to improve terminology control in cultural-heritage translation, provided that institutions maintain minimal terminology resources and lightweight evaluation procedures.
%U https://aclanthology.org/2026.eamt-1.49/
%P 774-786
Markdown (Informal)
[AI-assisted cultural heritage dissemination: Comparing NMT and glossary-augmented LLM translation in rock art documents](https://aclanthology.org/2026.eamt-1.49/) (Briva-Iglesias & Fernández, EAMT 2026)
ACL