AI-driven English translation: leveraging machine learning and deep learning for enhanced accuracy

Abstract

The quick growth of AI has greatly affected the field of machine translation, which in turn has resulted in more accurate and context-aware English translations. This article suggests an English translation framework that is based on the combination of machine learning (ML) and deep learning (DL). The study presents a variety of neural architectures including transformer-based models (e.g., BERT, GPT) and neural machine translation (NMT) systems by dealing with issues about translation fluency and contextual understanding. The authors use reinforcement learning (RL) and fine-tuning that are two of the machineries in their laboratory to bolster translation in the case of low-resource languages and technical writing. The suggested hybrid model leverages the power of both rule-based linguistic processing and AI technology for error avoidance and added real-time translation performance. As observed from the experimental results, the new model definitely has the edge as compared to the traditional statistical and rule-based systems. It gives out the highest BLEU and METEOR scores. This study is truly a solid basis for the way forward towards fully AI-driven multilingual translation systems.