@inproceedings{volker-etal-2025-salt,
    title = "{SALT} at {S}em{E}val-2025 Task 2: A {SQL}-based Approach for {LLM}-Free Entity-Aware-Translation",
    author = "Volker, Tom  and
      Pfister, Jan  and
      Hotho, Andreas",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.117/",
    pages = "852--864",
    ISBN = "979-8-89176-273-2",
    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."
}