Microsoft researchers create AI capable of translating Chinese on par with humans

Microsoft's Xuedong Huang claims to have 'hit human parity' in English-Chinese machine translation

A team of researchers at Microsoft have created what they believe is the first machine-translation system that can interpret sentences from Chinese to English with the same quality and accuracy as a bi-lingual human being.

The researchers, based in Microsoft's Asia and US labs, claimed in a blog post that their system achieved human parity on a commonly used test set of news stories, called Newstest2017, a system which was developed by a group of academics.

To ensure the results were both accurate and on par with what people would have translated, the team hired external bilingual human evaluators, who compared Microsoft's results to two independently produced human reference translations.

Technical fellow in charge of Microsoft's speech, natural language and machine translation efforts, Xuedong Huang, 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," Huang said. "We just didn't realize we'd be able to hit it so soon."

Huang, who also led the group that recently achieved human parity in a conversational speech recognition task, said the translation milestone was especially unique because of the possibilities it has for helping people understand each other better.

"The pursuit of removing language barriers to help people communicate better is fantastic," he said. "It's very, very rewarding."

Machine translation is a problem researchers have worked on for decades - and, experts say, for much of that time many believed human parity could never be achieved. Still, the researchers cautioned that the milestone does not mean that machine translation is a solved problem.

Ming Zhou, assistant managing director of Microsoft Research Asia and head of a natural language processing group said that while the team was thrilled to achieve the milestone on the dataset, he cautioned that there are still many challenges ahead, such as testing the system on real-time news stories.