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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, ...
By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder.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]
Apertium wiki (list of language pairs and licence information) Xerox Easy Translator Service (list of language pairs) Bing Translator Language List; Haitian Creole support in Bing/Microsoft Translator; Microsoft Research: Syntactically Informed Phrasal SMT; List of supported languages in Google Translate
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 .
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]
The Translate Toolkit is a localization and translation toolkit. It provides a set of tools for working with localization file formats and files that might need localization. The toolkit also provides an API on which to develop other localization tools.
OmegaT is another translation tool that can translate PO files. It is written in Java so it is available for multiple platforms (including Linux and Windows). It can be downloaded from SourceForge. GNU Gettext (Linux/Unix) used for the GNU Translation Project. Gettext also provides msgmerge that makes merging translations easy.
Use of a "do-not-translate" list, which has the same end goal – transliteration as opposed to translation. [39] still relies on correct identification of named entities. A third approach is a class-based model. Named entities are replaced with a token to represent their "class"; "Ted" and "Erica" would both be replaced with "person" class token.