Since then, Google Translate began using neural machine translation (NMT) in preference to its previous statistical methods (SMT) which had been used since October 2007, with its proprietary, in-house SMT technology. In November 2016, Google Neural Machine Translation system (GNMT) was introduced. Ng's work has led to some of the biggest breakthroughs at Google and Stanford. The Google Brain project was established in 2011 in the "secretive Google X research lab" by Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University Computer Science professor Andrew Ng. The GNMT network can undertake interlingual machine translation by encoding the semantics of the sentence, rather than by memorizing phrase-to-phrase translations. GNMT attempts to translate whole sentences at a time, rather than just piece by piece. With the large end-to-end framework, the system learns over time to create better, more natural translations. GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. GNMT improves 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. ![]() ![]() ![]() The total number of parameters has been variously described as over 160 million, approximately 210 million, 278 million or 380 million. The neural network consists of two main blocks, an encoder and a decoder, both of LSTM architecture with 8 1024-wide layers each and a simple 1-layer 1024-wide feedforward attention mechanism connecting them. Google Neural Machine Translation (GNMT) is a neural machine translation (NMT) system developed by Google and introduced in November 2016 that uses an artificial neural network to increase fluency and accuracy in Google Translate. System developed by Google to increase fluency and accuracy in Google Translate
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