The recent developments in machine translation research have shown a strong focus on enhancing the handling of linguistic nuances and improving the accuracy of pronoun and noun translations across different language pairs. Researchers are increasingly leveraging attention mechanisms and fine-tuning strategies to address specific translation challenges, such as pronoun disambiguation and the translation of attributive nouns. These advancements are not only improving the overall translation quality but also demonstrating the potential for targeted interventions to yield significant performance gains. Notably, the integration of semantic role labeling and mention attention modules is proving effective in resolving complex translation issues, particularly in contexts where language divergences are pronounced. These innovations suggest a promising trajectory for future research in machine translation, emphasizing the importance of context-aware and fine-grained approaches to translation accuracy.