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He estimates that the theory and practice of English-language translation had been dominated by submission, by fluent domestication. He strictly criticized the translators who in order to minimize the foreignness of the target text reduce the foreign cultural norms to target-language cultural values.
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.
Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base at run-time. It is essentially a translation by analogy and can be viewed as an implementation of a case-based reasoning approach to machine learning .
The following example can illustrate the general frame of RBMT: A girl eats an apple. Source Language = English; Demanded Target Language = German. Minimally, to get a German translation of this English sentence one needs: A dictionary that will map each English word to an appropriate German word.
For example, it might be trained just for Japanese-English and Korean-English translation, but can perform Japanese-Korean translation. The system appears to have learned to produce a language-independent intermediate representation of language (an "interlingua"), which allows it to perform zero-shot translation by converting from and to the ...
In such cases, a more dynamic translation may be used or a neologism may be created in the target language to represent the concept (sometimes by borrowing a word from the source language). The more the source language differs from the target language, the more difficult it may be to understand a literal translation without modifying or ...
But there's no way to group two English words producing a single French word. An example of a word-based translation system is the freely available GIZA++ package , which includes the training program for IBM models and HMM model and Model 6. [7] The word-based translation is not widely used today; phrase-based systems are more common.
For example: "Dan Smith" is the same person as "Daniel Smith" Semantic translation is very difficult if the terms in a particular data model do not have direct one-to-one mappings to data elements in a foreign data model. In that situation, an alternative approach must be used to find mappings from the original data to the foreign data elements.