AI-driven English translation: leveraging machine learning and deep learning for enhanced accuracy
Qing Zhao · International Journal of Information and Communication Technology · 2025
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.