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What is Neural Machine Translation (NMT)

Last year professionals had talked much about NMT. What is NMT?

Neural machine translation (NMT) is a machine translation approach that uses a large artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

All of the machine translation products (websites or apps) were based on algorithms using statistical methods to try to guess the best possible translation for a given word. This technology is called statistical machine translation.

However, one of the limitations of statistical machine translation is that it only translates words within the context of a few words before and after the translated word. For small sentences, it works pretty well. For longer ones, the translation quality could vary.

Now we have a new machine learning technology called deep learning or deep neural networks, one that tries to mimic how the human brain works (at least partially).

At a high-level, neural network translation works with in two stages:

— A first stage models the word that needs to be translated based on the context of this word (and its possible translations) within the full sentence, whether the sentence is 5 words or 20 words long.

— A second stage then translates this word model (not the word itself but the model the neural network has built of it), within the context of the sentence, into the other language.

One way to think about neural network-based translation could be to think of a fluent speaker in another language that would see a word, say “dog”. This would create the image of a dog in his or her brain, then this image would be associated to, for instance “le chien” in French. The neural network would intrinsically know that the word “chien” is masculine in French (“le” not “la”). But, if the sentence were to be “the dog just gave birth to six puppies” , it would picture the same dog with puppies nursing and would then automatically use “la chienne” (female form of “le chien”) when translating the sentence.

Because of this approach, sentences that are generated from a neural network based machine translation are usually better than statistical machine ones but also sound more fluent and natural, as if a human had translated them and not a machine.

Source: Microsoft

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