Microsoft revealed on Wednesday it has reached a monumental milestone in artificial intelligence-powered translation software, declaring the creation of a system that can translate news article sentences from Chinese to English just as good as a human.

“Hitting human parity in a machine translation task is a dream that all of us have had,” Xuedong Huang, technical fellow in charge of the company’s translation efforts, said in a statement. “We just didn’t realize we’d be able to hit it so soon.”

The breakthrough is the latest in a race to develop human-like translations. Google has improved its translation tools over time, parsing whole sentences with a November 2016 update. Microsoft’s tool, a version of which is available here, used over 2,000 sentences and new techniques.

“The pursuit of removing language barriers to help people communicate better is fantastic,” Huang said. “It’s very, very rewarding.”

Microsoft used ideas like deliberation networks to make translations more effective. Microsoft

Modern translation systems use deep neural networks to understand how a stentence is structured, enabling a more context-aware approach. This has served users well, but these systems tend to miss out some of the ways humans learn languages. The team was able to make the breakthrough by applying research from three groups in both Microsoft’s Beijing and Redmond labs, looking at how Microsoft could use human methods to make translation smarter.

The group introduced a number of methods into the mix:

Dual learning . Every time a sentence was translated from Chinese to English, it was translated back the other way from English to Chinese and vice versa. Ideally the two should match up, and it’s what humans tend to do right now when using an online translate tool.

. Every time a sentence was translated from Chinese to English, it was translated back the other way from English to Chinese and vice versa. Ideally the two should match up, and it’s what humans tend to do right now when using an online translate tool. Deliberation networks . The tool translates the same sentence over and over again, in a sort-of “editing” process where it irons out mistakes.

. The tool translates the same sentence over and over again, in a sort-of “editing” process where it irons out mistakes. Joint training . When the tool in one direction creates a sentence pair, the data is passed to the tool for the other direction to help both directions improve.

. When the tool in one direction creates a sentence pair, the data is passed to the tool for the other direction to help both directions improve. Agreement regularization. The system reads in both right and left directions. If the results are the same, that’s good. If they’re not, it’s maybe because something was lost along the way.

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