Innovations and Challenges in Neural Machine Translation: A Review

Mudasir Ashraf

Abstract: Neural Machine Translation NMT has revolutionized language translation through the use of deep learning techniques that offer greater accuracy and contextual understanding compared to earlier statistical models. This paper reviews recent advancements in NMT technologies such as attention mechanisms, transfer learning, and their applications in low resource languages. Despite these advancements, challenges remain in areas like the translation of idiomatic expressions, handling cultural nuances, and resource dependencies. This review highlights the potential of NMT to bridge global communication gaps while addressing its current limitations.

Keywords: Neural Machine Translation, deep learning, language models, contextual translation, multilingual networks