[bibshow file=machinetranslationturingtest.bib sort=firstauthor order=asc]
The U.S. government spent 4.5 billion USD from 1990 through 2009 on outsourcing translations and interpretations [bibcite key=USSpend]. If these translations were automated, much of this money could have been spent elsewhere. The research field of Machine Translation (MT) tries to develop systems capable of translating verbal language (i.e. speech and writing) from a certain source language to a target language.
Because verbal language is broad, allowing people to express a great number of things, one must take into account many factors when translating text from a source language to a target language. Three main difficulties when translating are proposed in [bibcite key=TTT1999]: the translator must distinguish between general vocabulary and specialized terms, as well as various possible meanings of a word or phrase, and must take into account the context of the source text.
Machine Translation systems must overcome the same obstacles as professional human translators in order to accurately translate text. To try to achieve this, researchers have had a variety of approaches over the past decades, such as [bibcite key=Gachot1989,Brown1990,Koehn2003]. At first, the knowledge-based paradigm was dominant. After promising results on a statistical-based system ([bibcite key=Brown1990,Brown1993]), the focus shifted towards this new paradigm.