Deep learning in machine translation: revolutionising language processing

Machine translation (MT) has undergone a remarkable transformation with the rise of deep learning methods, significantly improving translation accuracy and fluency. This paper examines how deep learning methodologies have influenced MT, particularly through the use of neural networks, Seq2Seq models, attention mechanisms, and transformer architectures. Traditional rule-based or statistical approaches have evolved into neural machine translation (NMT), leveraging large-scale data and advanced learning paradigms. The study highlights advancements, challenges, and future prospects, focusing on low-resource language translation, model bias, and computational efficiency. By analysing current developments and trends, this paper emphasises the revolutionary role of deep learning in enhancing multilingual communication through machine translation.