Large model driven adaptive English translation mechanism

Significant advancements have been made in the field of natural language processing thanks to the development of large-scale language models. However, existing translation methods often face limitations in handling contextual nuances, domain-specific terminology and evolving language usage. To address these challenges, we propose an adaptive English translation mechanism based on large models, incorporating adaptive feedback integration, domain-specific fine-tuning and a dynamic learning mechanism. Our approach leverages Transformer architectures to continuously refine translations by incorporating real-time user feedback and specialising in various domains. Evaluations conducted on data sets including WMT, OpenSubtitles, IWSLT and CCMT demonstrate significant improvements in BLEU, METEOR and TER scores compared to other models. Ablation studies confirm the contributions of each component, highlighting the importance of adaptive mechanisms in achieving superior translation performance. These findings indicate that our proposed method offers robust and adaptive solutions for diverse linguistic applications.