This paper presents our findings for SemEval 2025 Task 2, a shared task on entity-aware machine translation (EA-MT). The goal of this task is to develop translation models that can accurately translate English sentences into target languages, with a particular focus on handling named entities, which often pose challenges for MT systems. The task covers 10 target languages with English as the source. In this paper, we describe the different systems we employed, detail our results, and discuss insights gained from our experiments.

翻译：本文介绍了我们在SemEval 2025任务2（实体感知机器翻译共享任务）中的研究成果。该任务旨在开发能够准确将英语句子翻译为目标语言的翻译模型，特别关注处理命名实体的挑战——这类问题常对机器翻译系统构成困难。任务涵盖以英语为源语言的10种目标语言。本文详细阐述了我们采用的不同系统架构，具体分析了实验结果，并探讨了从实验中获得的启示。