English Translation Assistance System Integrating Machine Learning Algorithms

As computer-assisted translation systems become integral to daily life and learning, various issues have emerged, such as whether existing systems can deliver accurate translations across different sentence structures and lengths. This study focuses on optimizing neural machine translation technology to tackle challenges like low-resource language pairs and limited parallel corpora. We introduced an unsupervised neural machine translation model and assessed its performance based on BLEU scores, translation speed, loss value, translation accuracy, and average vocabulary similarity. The results revealed that the model excelled in semantic understanding and alignment, with an average similarity between 0.76 and 0.81. The model showed substantial improvements in translation performance, offering a novel approach to advancing neural machine translation technology.