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A round-trip translation is not testing one system, but two systems: the language pair of the engine for translating into the target language, and the language pair translating back from the target language. Consider the following examples of round-trip translation performed from English to Italian and Portuguese from Somers (2005):
C is the number of correct words, N is the number of words in the reference (N=S+D+C) The intuition behind 'deletion' and 'insertion' is how to get from the reference to the hypothesis. So if we have the reference "This is wikipedia" and hypothesis "This _ wikipedia", we call it a deletion. Note that since N is the number of words in the ...
The word-based translation is not widely used today; phrase-based systems are more common. Most phrase-based systems are still using GIZA++ to align the corpus [citation needed]. The alignments are used to extract phrases or deduce syntax rules. [11] And matching words in bi-text is still a problem actively discussed in the community.
Statistical and neural machine translation: Moses: Cross-platform: LGPL: No fee required: 4.0 [6] Yes: Drop-in replacement for Pharaoh, features factored translation models and decoding of confusion networks. Moses for Mere Mortals: Ubuntu Linux: GPL: No fee required: 2014-11-16: Yes: Free open source; based on Moses; translation memories ...
Among the top 100 words in the English language, which make up more than 50% of all written English, the average word has more than 15 senses, [134] which makes the odds against a correct translation about 15 to 1 if each sense maps to a different word in the target language. Most common English words have at least two senses, which produces 50 ...
Since the late 1980s, translation memory tools have been developed to increase productivity and make the whole translation process faster for the translator. In the 1990s, fuzzy matching began to take off as a prominent feature of TM tools, and despite some issues concerning the extra work involved in editing a fuzzy match "proposal", it is ...
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. This dataset can contain documents in many languages, but is in practice dominated by English text. [36]
Literal translation, direct translation, or word-for-word translation is the translation of a text done by translating each word separately without analysing how the words are used together in a phrase or sentence. [1] In translation theory, another term for literal translation is metaphrase (as opposed to paraphrase for an analogous translation).
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