Neural Machine Translation

For centuries people have been dreaming of easier communication with foreigners. The idea to teach computers to translate human languages is probably as old as computers themselves. The first attempts to build such technology go back to the 1950s. However, the first decade of research failed to produce satisfactory results, and the idea of machine translation was forgotten until the late 1990s. At that time, the internet portal AltaVista launched a free online translation service called Babelfish — a system that became a forefather for a large family of similar services, including Google Translate. At present, modern machine translation system rely on Machine Learning and Deep Learning techniques to improve the output and probably tackle the issues of understanding context, tone, language registers and informal expressions.

The techniques that were used until recently, including by Google Translate, were mainly statistical. Although quite effective for related languages, they tended to perform worse for languages from different families. The problem lies in the fact that they break down sentences into individual words or phrases and can span across only several words at a time while generating translations. Therefore, if languages have different words orderings, this method results in an awkward sequence of chunks of text.

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