Analysis of Learning Approaches for Machine Translation Systems
Subhashree Satpathy, Smita Prava Mishra, Ajit Kumar Nayak · 2019
Machine Translation(MT) is a part of Natural Language Processing(NLP). It is the method of translating Source Language(SL) text into Target Language(TL). The gap between computer programmer and linguist can be resolved with this system. The problems like lexical ambiguity, part of speech tagging, syntactic and structural ambiguity, synonym etc will arise while developing such systems. To develop a proper bilingual machine translation system for two natural languages is a challenging and demanding task for researchers. It is required to analyze the information as well as technology behind every natural language translation. This work represents the various approaches with current trends of machine translation system. In recent trends various machine learning approaches have been developed to tackle the above said problems. Most recently neural machine translation attain very good result by using different deep neural network techniques and machine learning algorithms.