Trends and Advances in Neural Machine Translation
Purva Kulkarni, Pravina Bhalerao, Kuheli Nayek, Rugved Vivek Deolekar · 2020 IEEE International Conference for Innovation in Technology (INOCON) · 2020
In the time of globalization, there is a requirement for conquering the language barrier and interfacing with an ever-increasing number of individuals. The value of speaking to customers in their language is tangible with retention and engagement rates up over 70% when there are effective localization workflows in place. To do this, integrating Neural Machine Translation into traditional localization workflows is an efficient way. In this paper, we break down different models, approaches, inception, principle development, and structures utilized in NMT to discover a productive strategy to make a translation system and identifying the advances and imperfections of the equivalent. It also talks about evolution of various NMT techniques leading to the current methodologies and numerous challenges which could be taken further for future research trends and improvements carried on them.