Google Translate's new neural machine translation system uses a large end-to-end artificial neural network capable of deep learning, in particular, long short-term memory networks.  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",  and the system did not invent its own universal language, but uses "the commonality found inbetween many languages".  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, followed by Indonesian, Bengali, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Tamil and Telugu languages in April. 
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