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  2. Comparison of different machine translation approaches

    en.wikipedia.org/wiki/Comparison_of_different...

    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 ...

  3. Untranslatability - Wikipedia

    en.wikipedia.org/wiki/Untranslatability

    Untranslatability is the property of text or speech for which no equivalent can be found when translated into another (given) language. A text that is considered to be untranslatable is considered a lacuna, or lexical gap.

  4. Apertium - Wikipedia

    en.wikipedia.org/wiki/Apertium

    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.

  5. Dictionary-based machine translation - Wikipedia

    en.wikipedia.org/wiki/Dictionary-based_machine...

    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 ...

  6. Rule-based machine translation - Wikipedia

    en.wikipedia.org/wiki/Rule-based_machine_translation

    Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively.

  7. Yandex Translate - Wikipedia

    en.wikipedia.org/wiki/Yandex_Translate

    The service uses a self-learning statistical machine translation, [3] developed by Yandex. [4] The system constructs the dictionary of single-word translations based on the analysis of millions of translated texts. In order to translate the text, the computer first compares it to a database of words.

  8. IBM alignment models - Wikipedia

    en.wikipedia.org/wiki/IBM_alignment_models

    In IBM Model 4, each word is dependent on the previously aligned word and on the word classes of the surrounding words. Some words tend to get reordered during translation more than others (e.g. adjective–noun inversion when translating Polish to English). Adjectives often get moved before the noun that precedes them.

  9. Interlingual machine translation - Wikipedia

    en.wikipedia.org/wiki/Interlingual_machine...

    The translation then proceeds by converting sentences from the first language into sentences closer to the target language through two stages. The system may also be set up such that the second interlingua uses a more specific vocabulary that is closer, or more aligned with the target language, and this could improve the translation quality.