Inproceedings,
SALT at SemEval-2025 Task 2: A SQL-based Approach for LLM-Free Entity-Aware-Translation
Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), page 852--864. Vienna, Austria, Association for Computational Linguistics, (July 2025)
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.
