A Collation of Machine Translation Approaches with Exemplified Comparison of Google and Bing Translators

Abstract

One of the vital areas of Natural Language Processing (NLP) is Machine Translation (MT). For providing benefit to Linguists, Sociologists and Computer Scientists, MT plays an important role. For translating one Natural Language (Source Language) into another Natural Language (Target Language), MT processes a Natural Language first. There are different languages speaking people and communities in all over the World. To fill the language gap between those people is the main objective of MT. Using different regional languages for the exchange of information between various regions is increasing, so the demand of day by day MT is also increasing reciprocatively. For this reason MT is the important subfield under Artificial Intelligence (AI) and NLP too. There are different approaches used to analyze, design and develop MT systems for automatic translation. The performance of an MT system depends on the approach used to design the system. This paper mostly focuses on different approaches of MT. Each approach has its own advantages, disadvantages and challenges. An analysis has also been done compare the performance and shortcomings of well known MT systems.