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  2. 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]

  3. 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.

  4. Reverso (language tools) - Wikipedia

    en.wikipedia.org/wiki/Reverso_(language_tools)

    Reverso is a French company specialized in AI-based language tools, translation aids, and language services. [2] These include online translation based on neural machine translation (NMT), contextual dictionaries, online bilingual concordances, grammar and spell checking and conjugation tools.

  5. I'm a translator who uses AI, but I'm not worried about it ...

    www.aol.com/im-translator-uses-ai-im-110101626.html

    A freelance translator says AI makes him more efficient at his job and it won't replace him.

  6. Naver Papago - Wikipedia

    en.wikipedia.org/wiki/Naver_Papago

    Naver Papago (Korean: 네이버 파파고), shortened to Papago and stylized as papago, is a multilingual machine translation cloud service provided by Naver Corporation. The name Papago comes from the Esperanto word for parrot , Esperanto being a constructed language.

  7. 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]

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