Machine Translation Systems Based on Classical-Statistical-Deep-Learning Approaches

Over recent years, machine translation has achieved astounding accomplishments. Machine become more evident with the need to understand information available on internet in different languages and due up-scaled exchange international trade. The enhanced computing speed advancements hardware components easy accessibility of monolingual bilingual data are significant factors that have added up boost success translation. This paper investigates models developed so far current state-of-the-art providing a solid understanding architectures comparative evaluation future directions for task. Because hybrid models, neural translation, statistical types utilized most frequently, it is essential an how each one functions. A comprehensive comprehension several approaches would be made possible as result this. In order advantages disadvantages various approaches, necessary conduct in-depth comparison variety benchmark datasets. accuracy translations from multiple compared using metrics such BLEU score, TER METEOR score.