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Submitted By:
Rakesh Kumar Purohit
Abstract
Machine translation (MT) is automated translation. It is the process by which computer software is used to translate a text from one natural language (such as English) to another (such as Spanish).
To process any translation, human or automated, the meaning of a text in the original (source) language must be fully restored in the target language, i.e. the translation. While on the surface this seems straightforward, it is far more complex. Translation is not a mere word-for-word substitution. A translator must interpret and analyze all of the elements in the text and know how each word may influence another. This requires extensive expertise in grammar, syntax (sentence structure), semantics (meanings), etc., in the source and target languages, as well as familiarity with each local region.
Introduction
Translation as being one of the most effective, if not the only, means of communication especially among cultures of different languages. Translation as a concept has existed hundred years ago, but it is only during the second half of the twentieth century that it emerged as an independent academic discipline called Translation Studies and taught at universities. A dire need for translation, as an academic discipline, has prompted specialised and theorists in the field to seek for more sophisticated methods and techniques for quick, cheap and effective translation. Thus, a new type of translation has emerged to compete with Human Translation; it is called Machine translation or the automatic translation.
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Machine translation, sometimes referred to by the abbreviation MT, is a sub-field of computational linguistics that investigates the use of computer software to translate text or speech from one natural language to another. At its basic level, MT performs simple substitution of words in one natural language for words in another. Using corpus techniques, more complex translations may be attempted, allowing for better handling of differences in linguistic typology, phrase recognition, and translation of idioms, as well as the isolation of anomalies. Current machine translation software often allows for customisation by domain or profession (such as weather reports) improving output by limiting the scope of allowable substitutions. This technique is particularly effective in domains where formal or formulaic language is used. It follows then that machine translation of government and legal documents more readily produces usable output than conversation or less standardised text. Improved output quality can also be achieved by human intervention: for example, some systems are able to translate more accurately if the user has unambiguously identified which words in the text are names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators, and in some cases can even produce output that can be used "as is". However, current systems are unable to produce output of the same quality as a human translator, particularly where the text to be translated uses casual language.
