Leveraging Machine Learning for Enhanced English Written Translation: Semantic Precision and Contextual Adaptation

Abstract

This meta-analysis explores the application of machine learning to expand the capabilities of written language translation of the English language with a focus on semantic accuracy and contextual adjustment. By consolidating 68 industry reports and peer-reviewed research, the study compares performance indicators such as BLEU scores, METEOR scores, and failure error rates. Findings indicate that transformer models outperform older statistical methods with a 2.5 improvement in translation accuracy and a 73% failure reduction in ambiguous phrases. Reinforcement learning-enhanced models also increase the optimization of legibility and fluency with human evaluation scores of over 90%. Efficiency in computations also increases with a 67% improvement in latency compared to older methods. Despite such advancements, concerns of bias, ethics, and sustainability exist. Future research must address such concerns as it also explores hybrid methods and multimodal integration of AI. The study presents a data-driven model for optimizing AI-based translation systems, validating the potential of such systems to increase global communication and language technology.