Researchers at the University of Amsterdam are using neural networks to help a statistical machine translation systems learn what all human translators know — that the best translation of a word often depends on the context.

Machine translation systems such as Google Translate or those at iTranslate4.eu guess how to translate words and phrases based on how often they appear in a large corpus of human-translated texts. Such tools are increasingly important as individuals and businesses seek to access information or buy products and services from other countries where different languages are spoken.

Statistical machine translation work by breaking sentences into phrase fragments and selecting the most likely translation for each fragment — a process that doesn’t always yield the best translation for the sentence as a whole in morphologically rich languages such as those where nouns are inflected for number, case and gender.

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