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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.

3. Google Translate - Wikipedia

   en.wikipedia.org/wiki/Google_Translate

   When Google Translate generates a translation proposal, it looks for patterns in hundreds of millions of documents to help decide on the best translation. By detecting patterns in documents that have already been translated by human translators, Google Translate makes informed guesses (AI) as to what an appropriate translation should be. [105]

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. Google improves Translate with offline AI - AOL

   www.aol.com/news/2018-06-12-google-translate...

   Google isn't going to sit idly by while Microsoft brings AI-based offline translation to your phone. The company is rolling out internet-free neural machine translation to its Translate apps for ...

6. 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 .

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]