A team of Microsoft researchers said Wednesday that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person.

In this paper "Achieving Human Parity on Automatic Chinese to English News Translation", researchers in the company's Asia and U.S. labs first address the problem of how to define and accurately measure human parity in translation. Ten they describe Microsoft's machine translation system and measure the quality of its translations on the widely used WMT 2017 news translation task from Chinese to English. The researchers found that their latest neural machine translation system has reached a new "state-of-the-art," and that the translation quality was at human parity when compared to professional human translations. They also foundnd that it significantly exceeded the quality of crowd-sourced non-professional translations.

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.

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," Huang said. "We just didn't realize we'd be able to hit it so soon."

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 that worked on the project, said that the team was thrilled to achieve the human parity milestone on the dataset. But he 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 the team 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.

Menezes said 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.

Researchers have worked on translation for years, they've recently achieved substantial breakthroughs by using a method of training AI systems called deep neural networks. To reach the human parity milestone on this dataset, three research teams in Microsoft's Beijing and Redmond, Washington, research labs worked together to add a number of other training methods that would make the system more fluent and accurate. In many cases, these new methods mimic how people improve their own work iteratively, by going over it again and again until they get it right.

One method they used is dual learning. Think of this as a way of fact-checking the system's work: Every time they sent a sentence through the system to be translated from Chinese to English, the research team also translated it back from English to Chinese. That's similar to what people might do to make sure that their automated translations were accurate, and it allowed the system to refine and learn from its own mistakes. Dual learning, which was developed by the Microsoft research team, also can be used to improve results in other AI tasks.

Another method, called deliberation networks, is similar to how people edit and revise their own writing by going through it again and again. The researchers taught the system to repeat the process of translating the same sentence over and over, gradually refining and improving the response.

The researchers also developed two new techniques to improve the accuracy of their translations, Zhou said.

One technique, called joint training, was used to iteratively boost the English-to-Chinese and Chinese-to-English translation systems. With this method, the English-to-Chinese translation system translates new English sentences into Chinese in order to obtain new sentence pairs. Those are then used to augment the training dataset that is going in the opposite direction, from Chinese to English. The same procedure is then applied in the other direction. As they converge, the performance of both systems improves.

Another technique is called agreement regularization. With this method, the translation can be generated by having the system read from left to right or from right to left. If these two translation techniques generate the same translation, the result is considered more trustworthy than if they don't get the same results. The method is used to encourage the systems to generate a consensus translation.