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Google Translate's neural machine translation system uses a large end-to-end artificial neural network capable of deep learning, in particular, long short-term memory networks. [41][42][43] GNMT improves the quality of translation because it uses an example-based machine translation (EBMT) method in which the system "learns from millions of examples. " It translates "whole sentences at a time, rather than just piece by piece. It uses this broader context to help it figure out the most relevant translation, which it then rearranges and adjusts to be more like a human speaking with proper grammar". GNMT's "proposed architecture" of "system learning" was first tested on over a hundred languages supported by Google Translate. With the end-to-end framework, "the system learns over time to create better, more natural translations. " The GNMT network is capable of interlingual machine translation, which encodes the "semantics of the sentence rather than simply memorizing phrase-to-phrase translations", [44] and the system did not invent its own universal language, but uses "the commonality found inbetween many languages". [45] GNMT was first enabled for eight languages: to and from English and Chinese, French, German, Japanese, Korean, Portuguese, Spanish and Turkish. In March 2017, it was enabled for Hindi, Russian and Vietnamese languages,[46] followed by Indonesian, Bengali, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Tamil and Telugu languages in April. [47]

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