Googles Neural Machine Translation System. Googles Neural Machine Translation System. 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.
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Unfortunately NMT systems are known to be computationally expensive both in training and in translation inference. Originally Google translates used narrow AI programs to perform translations. Google launched in 2016 Googles Neural Machine Translation system GNMT whose network includes 8 encoders and 8 decoders layers Wu et al 2016.
Neural Machine Translation NMT is an end-to-end learning approach for automated translation with the potential to overcome many of the weaknesses of conventional phrase-based translation systems.
The structure of the models is simpler than phrase-based models. Recently Google is working on hybrid models. Then in September of 2016 they announced a switch to a single system that uses artificial neural networks to provide translations. Neural Machine Translation is an end-to-end approach for automated translation with the hope of overcoming many of the weaknesses of conventional phrase-based translation systems.
