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In a first, Microsoft researchers have developed an artificial intelligence (AI) system that can translate news from Chinese to English with the same quality and accuracy as a human. Researchers said that their system achieved human parity on a commonly used test set of news stories, which was developed by a group of industry and academic partners. To ensure the results were both accurate and on par with what people would have done, the team hired external bilingual human evaluators, who compared Microsoft’s results to two independently produced human reference translations.

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Xuedong Huang, a technical fellow in charge of Microsoft’s speech, natural language and machine translation efforts, called it a major milestone in one of the most challenging natural language processing tasks. “Hitting human parity in a machine translation task is a dream that all of us have had. We just didn’t realise we’d be able to hit it so soon,” Huang said. The translation milestone was especially gratifying because of the possibilities it has for helping people understand each other better, he said. Machine translation is a problem researchers have worked on for decades and for much of that time many believed human parity could never be achieved.

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Ming Zhou, assistant managing director of Microsoft Research Asia, cautioned that there are still many challenges ahead, such as testing the system on real-time news stories. Arul Menezes, partner research manager of Microsoft’s machine translation team, said that they set out to prove that its systems could perform about as well as a person when it used a language pair – Chinese and English – for which there is a lot of data, on a test set that includes the more commonplace vocabulary of general interest news stories. “Given the best-case situation as far as data and availability of resources goes, we wanted to find out if we could actually match the performance of a professional human translator,” said Menezes, who helped lead the project.

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The research team can apply the technical breakthroughs they made for this achievement to Microsoft’s commercially available translation products in multiple languages. That will pave the way for more accurate and natural-sounding translations across other languages and for texts with more complex or niche vocabulary. To reach the human parity milestone, researchers worked added a number of other training methods that would make the system more fluent and accurate. These methods mimic how people improve their own work iteratively, by going over it again and again until they get it right. “Much of our research is really inspired by how we humans do things, said Tie-Yan Liu, a principal research manager with Microsoft Research Asia in Beijing. The researchers also developed two new techniques to improve the accuracy of their translations, Zhou said. These techniques could be useful for improving machine translation in other languages and situations as well. He said they also could be used to make other AI breakthroughs beyond translation.

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