MACHINE TRANSLATION TECHNIQUES AND CHALLENGES: A STUDY

Translation play a major role in communication. It acts as an intermediate between source language and target language. Technically Machine Translation involves the same process of conversion of text or sentence form of one language to another language. There is no human interaction in this text translation from one language to another; the conversion is being handled by a machine. Machine translation is an application of computers under Artificial Intelligence towards the task of translation of texts from one natural language to another. Several approaches have been adopted in the recent times for developing the machine translation system. Each has its own advantage and disadvantages. The machine translation systems use primarily different techniques: neural, statistical, and rule-based. A common approach is rule-based, which combines language and grammar with the use of dictionaries. There are many challenging aspects of machine translation like the large variety of languages, alphabets and grammars, the task to translate a sequence (a sentence for example) to a sequence is harder for a computer than working with numbers only, there is no one correct answer (e.g.: translating from a language without gender-dependent pronouns, he and she can be the same). This paper majorly concentrates on various machine learning techniques and its challenges.