Neural networks draw on context to improve machine translations

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. 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. To improve the word selection of such systems when translating into morphologically rich languages such as Russian, Bulgarian and German, the team used a neural network to analyze the words in context in the source language. Translating sentences into grammatically more complex languages is relatively easy for human translators because they understand the grammatical function of the word in a sentence.