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A DMT system is designed for a specific source and target language pair and the translation unit of which is usually a word. Translation is then performed on representations of the source sentence structure and meaning respectively through syntactic and semantic transfer approaches. A transfer-based machine translation system involves three ...
LMT, introduced around 1990, [2] is a Prolog-based machine-translation system that works on specially made bilingual dictionaries, such as the Collins English-German (CEG), which have been rewritten in an indexed form which is easily readable by computers. This method uses a structured lexical data base (LDB) in order to correctly identify word ...
The following table compares the number of languages which the following machine translation programs can translate between. (Moses and Moses for Mere Mortals allow you to train translation models for any language pair, though collections of translated texts (parallel corpus) need to be provided by the user.
In a rule-based machine translation system the original text is first analysed morphologically and syntactically in order to obtain a syntactic representation. This representation can then be refined to a more abstract level putting emphasis on the parts relevant for translation and ignoring other types of information.
A number of computer-assisted translation software and websites exists for various platforms and access types. According to a 2006 survey undertaken by Imperial College of 874 translation professionals from 54 countries, primary tool usage was reported as follows: Trados (35%), Wordfast (17%), Déjà Vu (16%), SDL Trados 2006 (15%), SDLX (4%), STAR Transit [fr; sv] (3%), OmegaT (3%), others (7%).
These models differ from an encoder-decoder NMT system in a number of ways: [35]: 1 Generative language models are not trained on the translation task, let alone on a parallel dataset. Instead, they are trained on a language modeling objective, such as predicting the next word in a sequence drawn from a large dataset of text.
Word-sense disambiguation concerns finding a suitable translation when a word can have more than one meaning. The problem was first raised in the 1950s by Yehoshua Bar-Hillel. [33] He pointed out that without a "universal encyclopedia", a machine would never be able to distinguish between the two meanings of a word. [34]
With its origin in the Georgetown machine translation effort, SYSTRAN was one of the few machine translation systems to survive the major decrease of funding after the ALPAC Report of the mid-1960s. The company was established in La Jolla in California to work on translation of Russian to English text for the United States Air Force during the ...