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Machine traslation (MT) accuracy has recently increased, due to better techniques and to the availability of larger parallel training sets. Statical (MT) are now able to traslate across a wide variety of language pairs. This article covers the basic elements of state-of-the-art, statical MT, including modeling, decoding, evaluation, and data preparation.

There are many aproaches to the machine traslation of Human Languages. Some aproaches requiere manual knowleadge ntry by highly skilled linguistics while others make a use of automatic training procedures. Some aproaches make use of abstract meaning representations, while other work at the level of word sustitutions. Many combinatins oh these dimensions have been explored – manual entry of large dictionaries, automatic learning of phrase substitution tables, semi-automatic construction of syntactic-transformation rules etc.

We can find some machine translators on the Internet:


*Computational Linguistics and Machine Traslation Research and Developement. Retrieved 30May 2009, 11:12 from http://www.fti.uab.es/tradumatica/revista/num4/articles/06/06central.htm

*Kevin Knight and Daniel Marcu. Retrieved 30 May 2009, 11:12 from  http://www.isi.edu/natural-language/mt/icassp05.pdf


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