S heffield GATE at S em E val-2025 Task 2: Multi-Stage Reasoning with Knowledge Fusion for Entity Translation (2025)

Abstract: This paper describes the machine translation system submitted to the SemEval-2025 Entity-Aware Machine Translation Task by the SheffieldGATE Team. We proposed a multi-agent entity-aware machine translation system that operates through three distinct reasoning stages: entity recognition, knowledge enhancement, and translation decision-making. The innovation in our approach lies in leveraging large language models to generate contextually relevant queries during the knowledge enhancement stage, extracting candidate entities and their translations from external knowledge bases. In the final translation decision-making stage, we employ fine-tuned large language models to denoise the retrieved knowledge, selecting the most relevant entity information to ensure accurate translation of the original text. Experimental results demonstrate our system’s effectiveness. In emEval-2025 Task 2, our system ranks first among all systems in Spanish entity translation metrics and third in Italian. For systems that do not use gold standard entity IDs during test set inference, ours achieves the highest overall scores across four language pairs: German, French, Italian, and Spanish.

## introduction machine translation has made significant progress in recent years, but it still faces substantial challenges when processing texts containing named entities. existing research focuses mainly on improving overall translation quality using metrics such as bleu @xcite , which treat all words equally. however, from a human comprehension perspective, different components within a sentence do not contribute equally to the quality of translation. named entities often carry information crucial for accurate communication, cultural transmission, and domain-specific translation @xcite . particularly in dynamic contexts such as social media, even state-of-the-art translation mod-els encounter significant difficulties when handling named entities @xcite . the challenges in named entity translation primarily stem from several key factors. first, the continuous emergence of new entities makes it difficult for translation systems based on fixed vocabularies to adapt. second, the correct translation of entities often depends on context. additionally, in informal settings such as social media, users often employ entities in creative ways, further complicating the translation task. these challenges underscore the necessity of developing specialized entity-aware machine translation approaches. to effectively address the challenges in named entity translation, this paper makes the following key contributions: first, we design a threestage reasoning framework based on large language models (llms) @xcite , specifically optimised for named entity translation. second, we propose innovative entity query generation mechanisms that effectively integrate information from external knowledge bases. experimental results show that our approach achieves significant improvements across multiple language pairs in named entity translation tasks. these findings provide new insights for the future development of entity-aware machine translation systems. the remainder of this paper is organised as follows. section 2 reviews related work, section 3 describes the system design, section 4 presents experimental results and analysis, and section 5 concludes the paper and explores directions for future work. ## related work named entity translation has emerged as a key challenge in machine translation. semeval-2025 task 2 @xcite marks the first entityaware machine translation evaluation task, providing a standardised assessment framework for re-search in this field. the task uses the xc-translate dataset @xcite , which represents the first large-scale manual annotated dataset focused on cross-cultural entity translation. based on these developments, we present a review of major research progress in this field. ## system description we present shef machine translation system a multi-agent entity-aware machine translation system that employs a multi-stage reasoning mechanism. through three key steps, entity extraction, knowledge enhancement, and translation decision, our system achieves high-quality entity translation. the overall architecture of the system is illustrated in figure 1 . ## conclusion this paper presents a multi-agent entity-aware machine translation system that addresses key challenges in named entity translation. our main contribution lies in designing a three-stage llm (large language model) reasoning framework (entity recognition, knowledge enhancement, and translation decision-making) specifically optimised for named entity translation, utilising innovative entity query generation mechanisms that effectively integrate information from external knowledge bases. experimental results demonstrate the effectiveness of our approach, which ranked first in spanish entity translation metrics in semeval-2025 task 2, and achieved the highest overall scores across four language pairs (german, french, italian, and spanish) among systems that do not use gold standard entity ids during test set inference. future work will focus on addressing the unique challenges of named entity translation in social media environments and developing more suitable approaches for informal texts and culture-specific expressions.