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The article argues that, what with the institutional character of a language and the important role of translation in shaping the cultural landscape of the Western world, popular and much expert opinion on translation - as a job or a product - might well be classable under three similar headings. An attempt is made at a trichotomy of 'positions' implicit in current reactions on translation concerning such criteria as language universals, translatability, equivalence, translation unit, etc.
The resultant typology is believed to reveal constant traits in popular thought on translation. If a typology is the output of lumpers and the input to hair splitters, a lot of 'hairsplitting' in the light of Harris' analysis, or similar historical work, may be called for to improve its reality content.
It creates a translation from an intermediate representation that simulates the meaning of the original sentence. Unlike interlingual MT, it depends partially on the language pair involved in the translation. The third method, interlingual machine translation, is a kind of rule-based machine-translation.
The source language is transformed into an interlingual language. The target language is then generated out of the interlingua. One of the major advantages of this system is that the interlingua becomes more valuable as the number of target languages it can be turned into increases.
However, the only interlingual machine translation system that has been made operational at the commercial level is the KANT system Nyberg and Mitamura, , which is designed to translate Caterpillar Technical English CTE into other languages. Using Caterpillar texts had the advantage of having an enormous load of already translated texts, and the fact that CTE is rather limited in scope: it only has to deal with technical language for heavy mobile equipment.
Using it to translate other subject matters, would be disastrous. The dictionary-based system uses a method based on dictionary entries, which means that the words will be translated as they are by a dictionary. This will make clear, of course, that a pure dictionary-based system can only give word-for-word translations, and therefore rather mediocre results — to put it mildly.
The statistical machine translation SMT uses bilingual text corpora.
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Where such corpora are available, good results can be achieved translating similar texts, but such corpora are still rare for many language pairs. Google switched to a statistical translation method in October In , Google improved its internal translation capabilities by using approximately billion words from United Nations materials to train their system, and the translation accuracy improved.
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Google Translate and similar statistical translation programs work by detecting patterns in hundreds of millions of documents that have previously been translated by humans and making intelligent guesses based on the findings. Generally, the more human-translated documents available in a given language, the more likely it is that the translation will be of good quality.
However, it turned out this is not always the case, rather to the surprise of Google. Newer approaches into Statistical Machine translation use minimal corpus size and instead focus on derivation of syntactic structure through pattern recognition, which puts higher stress on artificial intelligence.
Which explains why Google was disappointed.
Not to mention that a typical United Nations document deals with a limited set of subjects. Example-based machine translation is based on the idea of analogy. The corpus also contains texts that have already been translated. Given a sentence that is to be translated, sentences from this corpus are selected that contain similar sub-sentential components.