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AI-homework1-ensemble
AI-homework1-ensemble

... Google pre-processes a corpus of translated sources, including UN documents and public domain books. They use a Bayesian algorithm to statistically identify patterns in the translations. This creates a model, which can be used to create output to the user. The user may help to improve the model by s ...
Teaching Translation through Corpus
Teaching Translation through Corpus

... great value is probably correct, but quite beside the point. It certainly does not show that MT is impossible. First, translating literature requires special literary skill — it is not the kind of thing that the average professional translator normally attempts. Therefore, accepting the criticism do ...
But
But

... [7] M.T. Pilevar and H. Faili, "PersianSMT: A First Attempt to English-Persian Statistical Machine Translation", to appear in Proc. of 10th International Conference on statistical analysis of textual data (JADT 2010) ...
PPT - How do I get a website?
PPT - How do I get a website?

... machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until – in a visible future – the range of problems they can handle will be coextensive with the range to which human mind has been applied. More precisely: within 10 years a comp ...
Translation is very difficult
Translation is very difficult

... Shift from text to text and multi-media (word counts go down) Mobile user, hand held devices Real time/Just in time demand Cross-lingual translation challenges Balance of cost, timeliness and quality ...
Document
Document

... http://www.cs.berkeley.edu/~zadeh/ ...
INF 5820 Language technological applications
INF 5820 Language technological applications

< 1 2

Machine translation

Machine translation, sometimes referred to by the abbreviation MT (not to be confused with computer-aided translation, machine-aided human translation (MAHT) or interactive translation) is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another.On a basic level, MT performs simple substitution of words in one language for words in another, but that alone usually cannot produce a good translation of a text because recognition of whole phrases and their closest counterparts in the target language is needed. Solving this problem with corpus and statistical techniques is a rapidly growing field that is leading to better translations, handling differences in linguistic typology, translation of idioms, and the isolation of anomalies.Current machine translation software often allows for customization 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 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 proper names. With the assistance of these techniques, MT has proven useful as a tool to assist human translators and, in a very limited number of cases, can even produce output that can be used as is (e.g., weather reports).The progress and potential of machine translation have been debated much through its history. Since the 1950s, a number of scholars have questioned the possibility of achieving fully automatic machine translation of high quality. Some critics claim that there are in-principle obstacles to automatizing the translation process.
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