Language Translation Using Transformer and Encoder-Decoder

Smooth language translation is becoming more and more important in today's globalized society as it promotes efficient communication, knowledge sharing, and intercultural understanding. The study explores the domain of text translation, focusing on English to Russian and Hindi, as well as reverse translation. The primary objective is to enhance the accuracy and efficiency of machine translation systems for these language pairs. Recent advances in language translation are due to neural network-based models. We examine contemporary NMT systems' pre-processing techniques, attention mechanisms, and post-processing strategies, to understand their complicated relationship in producing high-quality translations. Furthermore, the paper explores the concept of transfer learning and multilingual models to improve translation quality across a wide range of language pairs. In conclusion, this paper provides a comprehensive overview of the current landscape of neural machine translation, highlighting the achievements, challenges, and future directions in the field of Machine Translation.