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Pipeline of Apertium machine translation system. This is an overall, step-by-step view how Apertium works. The diagram displays the steps that Apertium takes to translate a source-language text (the text we want to translate) into a target-language text (the translated text). Source language text is passed into Apertium for translation.
The functional block diagram can picture: [1] functions of a system pictured by blocks; input and output elements of a block pictured with lines; the relationships between the functions, and; the functional sequences and paths for matter and or signals [2] The block diagram can use additional schematic symbols to show particular properties ...
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 ...
A logic translation is a translation of a text into a logical system. For example, translating the sentence "all skyscrapers are tall" as ∀ x ( S ( x ) → T ( x ) ) {\displaystyle \forall x(S(x)\to T(x))} is a logic translation that expresses an English language sentence in the logical system known as first-order logic .
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.
The theory first appeared in an article published by linguist Hans Josef Vermeer in the German Journal Lebende Sprachen, 1978. [2]As a realisation of James Holmes’ map of Translation Studies (1972), [3] [4] skopos theory is the core of the four approaches of German functionalist translation theory [5] that emerged around the late twentieth century.
By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder. [10] GNMT improved on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. [2]
Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.