Neural machine translation (NMT) is an approach to machine translation that uses a large neural network. It departs from phrase-based statistical approaches that use separately engineered subcomponents. Neural machine translation (NMT) is not a drastic step beyond what has been traditionally done in statistical machine translation (SMT). Its main departure is the use of vector representations ("embeddings", "continuous space representations") for words and internal states. The structure of the models is simpler than phrase-based models. There is no separate language model, translation model, and reordering model, but just a single sequence model that predicts one word at a time. However, this sequence prediction is conditioned on the entire source sentence and the entire already produced target sequence.
Google, Yandex and Microsoft translation services now use NMT. Google uses Google Neural Machine Translation (GNMT) in preference to its previous statistical methods. Microsoft uses a similar technology for its speech translations (including Microsoft Translator live and Skype Translator). An open source neural machine translation system, OpenNMT, has been released by the Harvard NLP group. Yandex.Translator has a hybrid model: its translation provides a statistical model and a neural network. After this, the algorithm CatBoost, which is based on machine learning, will select the best of the obtained results Machine translation providers who also offer neural machine translation include Omniscien Technologies (formerly Asia Online), KantanMT, SDL, Globalese, and TransPerfect.
NMT models use deep learning and representation learning. They require only a fraction of the memory needed by traditional statistical machine translation (SMT) models. Furthermore, unlike conventional translation systems, all parts of the neural translation model are trained jointly (end-to-end) to maximize the translation performance. Deep neural machine translation is an extension of neural machine translation. Both use a large neural network with the difference that deep neural machine translation processes multiple neural network layers instead of just one. DeepL offers a generic machine translation system with deep learning AI systems while Omniscien Technologies provides customized deep neural machine translation (Deep NMT) and Systran offers Pure Neural Machine Translation with deep neural networks.
A bidirectional recurrent neural network (RNN), known as an encoder, is used by the neural network to encode a source sentence for a second RNN, known as a decoder, that is used to predict words in the target language.
Video Neural machine translation
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Source of article : Wikipedia
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