W ikidata-Driven Entity-Aware Translation: Boosting LLM s with External Knowledge (2025)

Abstract: This paper presents an entity-aware machine translation system that significantly improves named entity translation by integrating external knowledge from Wikidata with Large Language Models (LLMs). While LLMs demonstrate strong general translation capabilities, they struggle with named entities that require specific cultural or domain knowledge. We address this challenge through two approaches: retrieving multilingual entity representations using gold Wikidata IDs, and employing Relik, an information extraction tool, to automatically detect and link entities without gold annotations. Experiments across multiple language pairs show our system outperforms baselines by up to 63 percentage points in entity translation accuracy (m-ETA) while maintaining high overall translation quality. Our approach ranked 3rd overall and 1st among non-finetuned systems on the SemEval-2025 Task 2 leaderboard. Additionally, we introduced language-specific post-processing further enhances performance, particularly for Traditional Chinese translations.

## introduction machine translation (mt) has witnessed remarkable advancements in recent years, largely driven by neural approaches and, more recently, large language models (llms). despite these improvements, the accurate translation of named entities remains a significant challenge. named entities-proper names referring to people, famous landmarks, and cultural artifacts, which often require specialized handling that goes beyond standard translation procedures. the challenge of named entity translation is multifaceted. many entities demand specific localized forms in the target language (e.g., country names, famous landmarks). some entities, particularly those relating to cultural artifacts like books, movies, and products, may have official translations or established conventions in target languages that must be adhered to for accurate communication. traditional mt systems typically struggle with named entities for several reasons. first, named entities are often rare in training data, leading to poor representation in the model's parameters. second, ambiguity in entity references frequently requires contextual or world knowledge to resolve correctly. third, domain-specific or culturally-specific entities demand specialized knowledge that general mt systems may lack. these challenges become even more pronounced when translating between languages with different writing systems or culturally distant contexts. for instance, translating english entity names into languages like chinese, japanese, or arabic involves not just semantic transfer but also phonetic adaptation and cultural localization. to address these challenges, we explore how llms can be enhanced with external knowledge to better handle these challenging cases. our approaches leverage wikidata as an external knowledge source and demonstrate significant improvements in both entity translation accuracy and overall translation quality. our main contributions are as follows: ## related work the challenge of translating named entities has been a longstanding issue in machine translation research. our work addresses the task introduced by @xcite in semeval-2025 task 2, which highlights the limitations of existing translation models in handling named entities that require more than literal translation. previous work by @xcite established the foundation for this task by demonstrating the effectiveness of retrieval-augmented generation (rag) from multilingual knowledge graphs. while @xcite showed promising results with knowledge-enhanced translation in the biomedical domain, such approaches often struggle to generalize beyond specific domains to broader translation scenarios. the recent emergence of llm offers a potential solution to these generalization challenges. @xcite demonstrated impressive zero-shot translation abilities with llms, while @xcite and @xcite explored various prompting strategies to enhance their translation quality. these models show particular promise for low-resource language pairs @xcite and challenging linguistic phenomena @xcite . despite these advances, @xcite identified that llms remain prone to hallucinations when translating entities they have limited knowledge of, highlighting the need for augmentation with external knowledge sources. to effectively integrate external knowledge, robust entity recognition and linking capabilities are essential. existing systems like blink @xcite , @xcite , and crosslingual approaches @xcite provide valuable capabilities but often require significant computational resources. our work overcomes these constraints by leveraging relik @xcite , which provide en efficient method for identifying entities in text and connecting them to knowledge graph entries. our work builds upon these foundations, combining the strengths of llms with external knowledge retrieval to address the specific challenges of entityaware machine translation. by leveraging tools like relik for entity detection and wikidata for multilingual entity information, we create a system that significantly improves named entity translation. ## system description this section describes our entity-aware machine translation approach, which enhances llms with external knowledge to improve translation of sentences containing named entities. ## results and analysis in this section, we present our experimental results on the entity-aware machine translation task, comparing our system against baselines and analyzing factors affecting translation quality and entity handling across different settings. ## conclusion we introduced an entity-aware machine translation system that improves the translation of named entities by integrating external knowledge from wikidata. using gold wikidata ids or the relik tool for automatic entity extraction, our approach outperforms baseline models and ranks highly on the official leaderboard. this demonstrates the effectiveness of incorporating external knowledge to address the limitations of llms in handling named entities during translation tasks. this paper presented an effective approach to entity-aware machine translation by enhancing llms with external knowledge retrieval. we demonstrated that while state-of-the-art llms possess impressive general translation capabilities, they struggle significantly with named entity translation, particularly for culturally-specific entities or those requiring specialized knowledge. our experimental results across multiple language pairs confirm that integrating entity knowledge from wikidata substantially improves both entity translation accuracy and overall translation quality. the gold entity knowledge integration approach achieved near-optimal performance (m-eta of 89.0%, comet of 94.8%), ranking among the top systems in the semeval-2025 task 2 competition. more importantly, our practical relikbased approach, which automatically identifies and links entities without requiring gold annotations, achieved competitive results (m-eta of 71.4%, comet of 92.8%) while being applicable to realworld translation scenarios. analysis of our approach revealed several key insights: (1) providing complete entity information in both source and target languages is crucial for accurate entity translation, (2) automatic entity detection with knowledge retrieval is highly effective for practical applications, and (3) targeted post-processing for specific language challenges, such as traditional chinese character conversion, can yield substantial gains. our work contributes to the ongoing efforts to make machine translation more reliable for realworld scenarios where accurate handling of named entities is essential for effective cross-cultural communication.