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    <abstract>Accurate terminology is a non-negotiable requirement in industrial localization processes: a single mistranslated domain term can violate contractual obligations and erode client trust.We present AIDA\textsubscriptterm, a deployed multi-agent LLM pipeline that orchestrates four specialized agents—Analysis, Translation, Post-editing, and Review—for terminology-constrained machine translation.The system introduces terminology-aware pre-analysis, explicit glossary injection at every pipeline stage, and a reasoning-enabled Review agent.We evaluate six configurations on the WMT25 Terminology Translation benchmark (Track 1: en࢐de/es/ru, IT domain), enabling systematic ablation of each design choice.Our best configuration achieves 99.4% average terminology accuracy while attaining the highest ChrF2++ scores across all three language pairs, outperforming all 20 systems submitted to the shared task.Unlike other multi-agent approaches in WMT25 that rely on generate-and-select strategies, AIDA\textsubscriptterm is the first to apply a role-specialized sequential pipeline to terminology-constrained MT, and is deployed with native XLIFF integration for seamless CAT tool interoperability.The system processes thousands of terminology-constrained requests daily at a large localization provider.</abstract>
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%0 Conference Proceedings
%T Multi-Agent Orchestration for Terminology-Constrained Machine Translation in Industrial Localization
%A Rosa, Emanuele Di
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F rosa-2026-multi
%X Accurate terminology is a non-negotiable requirement in industrial localization processes: a single mistranslated domain term can violate contractual obligations and erode client trust.We present AIDA\textsubscriptterm, a deployed multi-agent LLM pipeline that orchestrates four specialized agents—Analysis, Translation, Post-editing, and Review—for terminology-constrained machine translation.The system introduces terminology-aware pre-analysis, explicit glossary injection at every pipeline stage, and a reasoning-enabled Review agent.We evaluate six configurations on the WMT25 Terminology Translation benchmark (Track 1: en࢐de/es/ru, IT domain), enabling systematic ablation of each design choice.Our best configuration achieves 99.4% average terminology accuracy while attaining the highest ChrF2++ scores across all three language pairs, outperforming all 20 systems submitted to the shared task.Unlike other multi-agent approaches in WMT25 that rely on generate-and-select strategies, AIDA\textsubscriptterm is the first to apply a role-specialized sequential pipeline to terminology-constrained MT, and is deployed with native XLIFF integration for seamless CAT tool interoperability.The system processes thousands of terminology-constrained requests daily at a large localization provider.
%U https://aclanthology.org/2026.acl-industry.63/
%P 917-926

Markdown (Informal)

[Multi-Agent Orchestration for Terminology-Constrained Machine Translation in Industrial Localization](https://aclanthology.org/2026.acl-industry.63/) (Rosa, ACL 2026)

• Multi-Agent Orchestration for Terminology-Constrained Machine Translation in Industrial Localization (Rosa, ACL 2026)

ACL

• Emanuele Di Rosa. 2026. Multi-Agent Orchestration for Terminology-Constrained Machine Translation in Industrial Localization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 917–926, San Diego, California, USA. Association for Computational Linguistics.