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  2. Neural machine translation - Wikipedia

    en.wikipedia.org/wiki/Neural_machine_translation

    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]

  3. Comparison of different machine translation approaches

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

    A rendition of the Vauquois triangle, illustrating the various approaches to the design of machine translation systems.. The direct, transfer-based machine translation and interlingual machine translation methods of machine translation all belong to RBMT but differ in the depth of analysis of the source language and the extent to which they attempt to reach a language-independent ...

  4. Comparison of machine translation applications - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_machine...

    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.

  5. Skopos theory - Wikipedia

    en.wikipedia.org/wiki/Skopos_theory

    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.

  6. Machine translation - Wikipedia

    en.wikipedia.org/wiki/Machine_translation

    Machine translation is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Early approaches were mostly rule-based or statistical. These methods have since been superseded by neural machine translation [1] and large language models ...

  7. Google Neural Machine Translation - Wikipedia

    en.wikipedia.org/wiki/Google_Neural_Machine...

    Google Translate's NMT system uses a large artificial neural network capable of deep learning. [1] [2] [3] By using millions of examples, GNMT improves the quality of translation, [2] using broader context to deduce the most relevant translation. The result is then rearranged and adapted to approach grammatically based human language. [1]

  8. Interlingual machine translation - Wikipedia

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

    Demonstration of the languages which are used in the process of translating using a bridge language. Interlingual machine translation is one of the classic approaches to machine translation. In this approach, the source language, i.e. the text to be translated is transformed into an interlingua, i.e., an abstract language-independent ...

  9. Task-based language learning - Wikipedia

    en.wikipedia.org/wiki/Task-based_language_learning

    Task-supported language teaching (TSLT) also incorporates tasks as a central part of the lesson. However, while TBLT follows the pre-task, task, and post-task sequence, TSLT uses Present-Practice-Produce model as its backbone, then adds a task as an activity to practice linguistic items in the production stage. [26]