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Machine Translation

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.

Machine translation can use a method based on linguistic rules, which means that words will be translated in a linguistic way — the most suitable (orally speaking) words of the target language will replace the ones in the source language.
The Machine Translation (MT) project at Microsoft Research is focused on creating MT systems and technologies that cater to the multitude of translation scenarios today. Data driven systems, in particular those with a statistical core engine, have proven to be the most efficient, due to their ability to adapt to a wide domain coverage and being trained in new language pairs within a matter of weeks. This team works closely with research and development partners worldwide,  making the system accessible to a variety of products and services.
The field of machine translation has changed remarkably little since its earliest days in the fifties. The issues that divided researchers then remain the principal bones of contention today. The first of these concerns the distinction between that so-called interlingual and the transfer approach to the problem. The second concerns the relative importance of linguistic matters as opposed to common sense and general knowledge. The only major new lines of investigation that have emerged in recent years have involved the use of existing translations as a prime source of information for the production of new ones. One form that this takes is that of example-based machine translation in which a system of otherwise fairly conventional design is able to refer to a collection of existing translations.

Retrieved on the 11th of May 2008

*Microsoft Corporation-Microsoft Research:*Martin

Palo Alto Research Center, Palo Alto, California:

*Wikipedia-The free enyclopedia, John Hutchins:


mayo 11, 2008 - Posted by | Human Language Technologies |

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