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    <abstract>Entity-Aware Machine Translation (EAMT) aims to enhance the accuracy of machine translation (MT) systems in handling named entities, including proper names, domain-specific terms, and structured references. Conventional MT models often struggle to accurately translate these entities, leading to errors that affect comprehension and reliability. In this paper, we present a promising approach for SemEval 2025 Task 2, focusing on improving EAMT in ten target languages. The methodology is based on two complementary strategies: (1) multilingual Named Entity Recognition (NER) and structured knowledge bases for preprocessing and integrating entity translations, and (2) large language models (LLMs) enhanced with optimized prompts and validation mechanisms to improve entity preservation. By combining structured knowledge with neural approaches, this system aims to mitigate entity-related translation errors and enhance the overall performance of MT models. Among the systems that do not use gold information, retrieval-augmented generation (RAG), or fine-tuning, our approach ranked 1st with the second strategy and 3rd with the first strategy.</abstract>
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%0 Conference Proceedings
%T FII the Best at SemEval 2025 Task 2: Steering State-of-the-art Machine Translation Models with Strategically Engineered Pipelines for Enhanced Entity Translation
%A Grigorita, Delia -. Iustina
%A Pricop, Tudor -. Constantin
%A Suteu, Sergio -. Alessandro
%A Gifu, Daniela
%A Trandabat, Diana
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F grigorita-etal-2025-fii
%X Entity-Aware Machine Translation (EAMT) aims to enhance the accuracy of machine translation (MT) systems in handling named entities, including proper names, domain-specific terms, and structured references. Conventional MT models often struggle to accurately translate these entities, leading to errors that affect comprehension and reliability. In this paper, we present a promising approach for SemEval 2025 Task 2, focusing on improving EAMT in ten target languages. The methodology is based on two complementary strategies: (1) multilingual Named Entity Recognition (NER) and structured knowledge bases for preprocessing and integrating entity translations, and (2) large language models (LLMs) enhanced with optimized prompts and validation mechanisms to improve entity preservation. By combining structured knowledge with neural approaches, this system aims to mitigate entity-related translation errors and enhance the overall performance of MT models. Among the systems that do not use gold information, retrieval-augmented generation (RAG), or fine-tuning, our approach ranked 1st with the second strategy and 3rd with the first strategy.
%U https://aclanthology.org/2025.semeval-1.78/
%P 558-565

Markdown (Informal)

[FII the Best at SemEval 2025 Task 2: Steering State-of-the-art Machine Translation Models with Strategically Engineered Pipelines for Enhanced Entity Translation](https://aclanthology.org/2025.semeval-1.78/) (Grigorita et al., SemEval 2025)

• FII the Best at SemEval 2025 Task 2: Steering State-of-the-art Machine Translation Models with Strategically Engineered Pipelines for Enhanced Entity Translation (Grigorita et al., SemEval 2025)

ACL

• Delia - Iustina Grigorita, Tudor - Constantin Pricop, Sergio - Alessandro Suteu, Daniela Gifu, and Diana Trandabat. 2025. FII the Best at SemEval 2025 Task 2: Steering State-of-the-art Machine Translation Models with Strategically Engineered Pipelines for Enhanced Entity Translation. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 558–565, Vienna, Austria. Association for Computational Linguistics.