Contextual Language Modeling for Enhanced Machine Translation: Leveraging Long-Context Representations

This research presents a groundbreaking model for machine translation that employs long-context representations to effectively capture intricate linguistic dependencies, significantly improving translation quality across various language pairs. By overcoming the limitations of traditional architectures, such as Transformer and LSTM-based seq2seq models, this innovative approach achieves enhanced semantic coherence and contextual understanding. Notably, the model attained a BLEU score of 41.3% for the English-French pair, surpassing Transformer (38.2%) and LSTM (32.5%), alongside a BERTScore of 89%, compared to Transformer's 85%. Performance metrics, including Precision, Recall, and F1 Score, consistently exceeded 85%, reflecting the model's reliability in multilingual contexts. Furthermore, its computational efficiency allows for real-time inference, making it ideal for large-scale applications. The model's adaptability to both high- and low-resource languages highlights its potential for global communication. This study sets a new benchmark in machine translation, paving the way for future research on multilingual data and fine-tuning techniques.