San-Eng: Sanskrit to English Translator using Machine Learning

Abstract

In the past decade, machine learning has made great strides in improving automatic translation. In general, machine learning algorithms have been able to achieve better translation quality by learning from large amounts of data. One approach to machine translation is to use a statistical machine translation (SMT) system. SMT systems learn to translate by statistical methods, using a large parallel corpus of text in multiple languages. The most successful SMT systems are based on the translation model known as the phrase-based translation model. In recent years, a new approach to machine translation has emerged, known as neural machine translation (NMT). NMT systems use artificial neural networks to learn to translate. NMT systems have shown to be very successful in translating between languages that are closely related, such as English and Hindi or Reverse is also true. In recent years, a new approach to machine translation has emerged, known as neural machine translation (NMT). NMT systems use artificial neural networks to learn to translate. NMT systems have shown to be very successful in translating between languages that are closely related, such as English and Hindi or Reverse is also true