SALT at S em E val-2025 Task 2: A SQL -based Approach for LLM -Free Entity-Aware-Translation (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.

## introduction despite ever-progressing language model capabilities, they continue to struggle with tasks requiring precise factual knowledge and cross-cultural understanding @xcite @xcite . one such challenge is named entity translation, where direct word-for-word approaches often miss cultural nuances (díaz-millón and olvera-lobo; @xcite . accurate entity-aware translation is essential for preserving meaning across languages. for example, roald dahl's the witches became hexen hexen ("witches bewitch") in german -a choice no model could infer from the source alone. recent advances in machine translation, particularly with large language models (llms), have greatly improved translation quality @xcite @xcite . however, translating cultur-ally adapted entity names remains difficult @xcite due to: 1) the need for transcreation-creative adaptation beyond literal translation @xcite 2) constantly emerging entities, which frozen llm weights cannot capture @xcite 3) variability in translation based on cultural, geographical, or temporal context @xcite the semeval-2025 task 2 on entity-aware machine translation (ea-mt) @xcite addresses this challenge by requiring systems to translate english sentences with named entities into ten target languages, spanning both latin (e.g., german, spanish) and non-latin scripts (e.g., japanese, arabic). for example, "what year did roald dahl release the novel the witches?" should be translated into german as "in welchem jahr veröffentlichte roald dahl den roman hexen hexen?"using the localized title. we present salt foot_0 (sql-based approach for llm-free entity-aware-translation), a simple yet effective solution to this challenge. while based on neural machine translation @xcite , salt avoids the complexity of additional neural components for entity handling and the parametric overhead of modern llms. instead, it leverages efficient sql-based entity retrieval, constrained neural translation via logit biasing, and explicit entity annotations. our system achieves state-of-the-art results among approaches without gold-standard data (e.g., wikidata ids) during testing, while maintaining significantly lower computational costs @xcite . our key contributions are: • a parameter-efficient entity-aware translation approach that achieves competitive results without additional trainable components beyond the base model. ## system description we propose a surprisingly simple yet effective twostage pipeline for entity-aware machine translation, that focuses on parameter efficiency. our approach consists of (3.1) a deterministic entity retrieval and translation lookup phase, followed by (3.2) a constrained neural translation step. despite exploring more complex methods in our ablation studies (section 6), this streamlined approach achieves highly competitive results with significantly lower computational cost. ## results our sql-based retrieval and constrained neural translation prove effective across all languages, outperforming both the top overall llm-based system by fii-uaic-sai (78.17) and the next best non-llm-based system by team zero (47.79). 6 the substantial 25-point gain over the baseline by @xcite underscores the value of our small but substantial methodological refinements outlined above. performance is strongest on languages with substantial mintaka training data (arabic, german, spanish, french, italian, and japanese), where m-eta scores range from 72.20% to 81.72%. chinese remains the most challenging (45.27% m-eta), with fii-uaic-sai surpassing our system by 17.23 points. for reference, we include the top-performing shared task submission (pingan_team) in gray, achieving a remarkable 91.79 overall using gold wikidata annotations at inference. while this limits real-world applicability, it demonstrates the high potential upper bound of the dataset. ## ablations and analyses having established the effectiveness of our minimal approach, we now investigate both the validity of our design choices and the potential gains in more complex alternatives, allowing us to quantify trade-offs while confirming our core architectural decisions. ## conclusion we introduced salt , a parameter-efficient, entity-aware machine translation approach that achieves first place among models not using gold data during translation. our ablation studies challenge several intuitive assumptions: simpler retrieval methods often outperform complex ones; clarity trumps coverage when providing entity candidates; and lightweight techniques like logit biasing can match parameter-heavy approaches. the narrow gap between our system and llm-based alternatives (0.62 pp) demonstrates that parameter efficiency need not sacrifice translation quality. our work counters the prevailing trend toward everlarger models, suggesting that targeted knowledge integration can be more effective than simply scaling parameters for specialized translation tasks. whilst achieving state-of-the-art results without gold-standard data, challenges remain, particularly in our chinese translations and in generalizing to more diverse real-world translation scenarios.