Google’s researchers think their system achieves this breakthrough by finding a common ground whereby sentences with the same meaning are represented in similar ways regardless of language – which they say is an example of an “interlingua”. His team and another group at Karlsruhe Institute of Technology in Germany have independently published similar studies working towards neural translation systems that can handle multiple language combinations. “This is a big advance,” says Kyunghyun Cho at New York University. This capability may enable Google to quickly scale the system to translate between a large number of languages. With a little tinkering, however, Google has extended its system so that it can handle multiple pairs – and it can translate between two languages when it hasn’t been directly trained to do so.įor example, if the neural network has been taught to translate between English and Japanese, and English and Korean, it can also translate between Japanese and Korean without first going through English. This system is now in action for eight of the most common language pairs on which Google Translate works.Īlthough neural machine-translation systems are fast becoming popular, most only work on a single pair of languages, so different systems are needed to translate between others. In September, Google Translate unveiled a new system that uses a neural network to work on entire sentences at once, giving it more context to figure out the best translation. Traditional machine-translation systems break sentences into words and phrases, and translate each individually. To do this, it seems to have created its own artificial language. The online translation tool recently started using a neural network to translate between some of its most popular languages – and the system is now so clever it can do this for language pairs on which it has not been explicitly trained.
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