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  2. Google Translate - Wikipedia

    en.wikipedia.org/wiki/Google_Translate

    Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. [3]

  3. 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.

  4. Anuvaad (Document Translation Platform) - Wikipedia

    en.wikipedia.org/wiki/Anuvaad_(Document...

    Free software portal; Project Anuvaad is an open-source Document Translation platform to translate documents in Indic languages at scale. Anuvaad provides editing capabilities with maker-checker flow along with plug & play NMT models.

  5. DeepL Translator - Wikipedia

    en.wikipedia.org/wiki/DeepL_Translator

    DeepL Translator is a neural machine translation service that was launched in August 2017 and is owned by Cologne-based DeepL SE. The translating system was first developed within Linguee and launched as entity DeepL. It initially offered translations between seven European languages and has since gradually expanded to support 33 languages.

  6. 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.

  7. Yandex Translate - Wikipedia

    en.wikipedia.org/wiki/Yandex_Translate

    Previously, machine translation was based on "the meaning of the text" model: take any language, translate the words in the universal language of the senses, and then translate these meanings in the words of another language – and obtain the translated text. This model prevailed in the 1970s-1980s and automated in the 1990s.

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