@inproceedings{castaldo-etal-2026-unior,
title = "{U}ni{OR}-{MT}: An Agentic Pipeline for Terminology Translation",
author = "Castaldo, Antonio and
Speranza, Giulia and
Staiano, Maria Carmen and
Giommarelli, Petra and
Monti, Johanna and
Di Buono, Maria Pia",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.113/",
pages = "1779--1790",
ISBN = "979-8-89176-474-3",
abstract = "We present UniOR-MT, an agentic terminology-aware machine translation pipeline developed for the WMT 2026 Terminology Translation Shared Task. We compare five progressively enriched translation paths, each adding one component to isolate its individual contribution. We additionally submit a mixed system that selects, among the five candidate paths, the best candidate using a combined measure of terminology accuracy and document-level quality estimation. Evaluation results show that incorporating a bilingual glossary provides the most consistent benefit, while additional post-editing steps yield limited improvements, and can even degrade translation quality for Spanish{--}Basque in the official evaluation. The final system, which includes iterative QA feedback and a stronger fallback model, achieves the highest docCOMET scores in our internal evaluation."
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<abstract>We present UniOR-MT, an agentic terminology-aware machine translation pipeline developed for the WMT 2026 Terminology Translation Shared Task. We compare five progressively enriched translation paths, each adding one component to isolate its individual contribution. We additionally submit a mixed system that selects, among the five candidate paths, the best candidate using a combined measure of terminology accuracy and document-level quality estimation. Evaluation results show that incorporating a bilingual glossary provides the most consistent benefit, while additional post-editing steps yield limited improvements, and can even degrade translation quality for Spanish–Basque in the official evaluation. The final system, which includes iterative QA feedback and a stronger fallback model, achieves the highest docCOMET scores in our internal evaluation.</abstract>
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%0 Conference Proceedings
%T UniOR-MT: An Agentic Pipeline for Terminology Translation
%A Castaldo, Antonio
%A Speranza, Giulia
%A Staiano, Maria Carmen
%A Giommarelli, Petra
%A Monti, Johanna
%A Di Buono, Maria Pia
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F castaldo-etal-2026-unior
%X We present UniOR-MT, an agentic terminology-aware machine translation pipeline developed for the WMT 2026 Terminology Translation Shared Task. We compare five progressively enriched translation paths, each adding one component to isolate its individual contribution. We additionally submit a mixed system that selects, among the five candidate paths, the best candidate using a combined measure of terminology accuracy and document-level quality estimation. Evaluation results show that incorporating a bilingual glossary provides the most consistent benefit, while additional post-editing steps yield limited improvements, and can even degrade translation quality for Spanish–Basque in the official evaluation. The final system, which includes iterative QA feedback and a stronger fallback model, achieves the highest docCOMET scores in our internal evaluation.
%U https://aclanthology.org/2026.wmt-main.113/
%P 1779-1790
Markdown (Informal)
[UniOR-MT: An Agentic Pipeline for Terminology Translation](https://aclanthology.org/2026.wmt-main.113/) (Castaldo et al., WMT 2026)
ACL
- Antonio Castaldo, Giulia Speranza, Maria Carmen Staiano, Petra Giommarelli, Johanna Monti, and Maria Pia Di Buono. 2026. UniOR-MT: An Agentic Pipeline for Terminology Translation. In Proceedings of the Eleventh Conference on Machine Translation, pages 1779–1790, Budapest, Hungary. Association for Computational Linguistics.