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        <title>SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-Translation</title>
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    <abstract>Entity-aware machine translation faces significant challenges when translating culturally-adapted named entities that require knowledge beyond the source text. We present SALT (SQL-based Approach for LLM-Free Entity-Aware-Translation), a parameter-efficient system for the SemEval-2025 Task 2. Our approach combines SQL-based entity retrieval with constrained neural translation via logit biasing and explicit entity annotations. Despite its simplicity, it achieves state-of-the-art performance (First Place) among approaches not using gold-standard data, while requiring far less computation than LLM-based methods. Our ablation studies show simple SQL-based retrieval rivals complex neural models, and strategic model refinement outperforms increased model complexity. SALT offers an alternative to resource-intensive LLM-based approaches, achieving comparable results with only a fraction of the parameters.</abstract>
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%0 Conference Proceedings
%T SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-Translation
%A Völker, Tom
%A Pfister, Jan
%A Hotho, Andreas
%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 volker-etal-2025-salt
%X Entity-aware machine translation faces significant challenges when translating culturally-adapted named entities that require knowledge beyond the source text. We present SALT (SQL-based Approach for LLM-Free Entity-Aware-Translation), a parameter-efficient system for the SemEval-2025 Task 2. Our approach combines SQL-based entity retrieval with constrained neural translation via logit biasing and explicit entity annotations. Despite its simplicity, it achieves state-of-the-art performance (First Place) among approaches not using gold-standard data, while requiring far less computation than LLM-based methods. Our ablation studies show simple SQL-based retrieval rivals complex neural models, and strategic model refinement outperforms increased model complexity. SALT offers an alternative to resource-intensive LLM-based approaches, achieving comparable results with only a fraction of the parameters.
%U https://aclanthology.org/2025.semeval-1.117/
%P 852-864

Markdown (Informal)

[SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-Translation](https://aclanthology.org/2025.semeval-1.117/) (Völker et al., SemEval 2025)

• SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-Translation (Völker et al., SemEval 2025)

ACL

• Tom Völker, Jan Pfister, and Andreas Hotho. 2025. SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-Translation. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 852–864, Vienna, Austria. Association for Computational Linguistics.