In addition, it could enable more accurate and natural translations across other languages in the future. “This is an area where machine translation research can apply to the whole field of AI research,” Zhou said. Zhou said the techniques used to achieve the milestone won’t be limited to machine translations. The researchers said they used methods including dual learning for fact-checking translations deliberation networks, to repeat translations and refine them and new techniques like joint training, to iteratively boost English-to-Chinese and Chinese-to-English translation systems and agreement regularization, which can generate translations by reading sentences both left-to-right and right-to-left. Microsoft’s researchers also added their own training methods to the system to improve its accuracy – things they equate to how people go over their own work time and again to make sure it’s right. systems, allowed the researchers to create more fluent and natural-sounding translations that take into account broader context that the prior approaches, called statistical machine translation. contributed to researchers achieving this milestone, Microsoft also notes.ĭeep neural networks, a method of training A.I. “People can use different words to express the exact same thing, but you cannot necessarily say which one is better.” “Machine translation is much more complex than a pure pattern recognition task,” said Ming Zhou, assistant managing director of Microsoft Research Asia and head of a natural language processing group that worked on the project. To really understand what’s being said in longer articles, you’d need a person’s help.īut even different human translators may translate a sentence in a slightly different way, with neither being wrong. and speech recognition have allowed voice assistants to find their way onto our smartphones and in our homes where help consumers with everyday computing tasks, controlling smart home devices, and for news and entertainment purposes.īut asking for a machine translation of a web page or news article still often renders the same hard-to-understand mess of words that, at best, gives you a general idea about what’s being said, but is nearly impossible to grasp with any deep comprehension. Getting a machine to understand language at this scale is far more complicated than speech recognition – something that’s seen a number of advances in recent years. “We just didn’t realize we’d be able to hit it so soon.” “Hitting human parity in a machine translation task is a dream that all of us have had,” said Xuedong Huang, a technical fellow in charge of Microsoft’s speech, natural language and machine translation efforts, in Microsoft’s blog post. Many have even believed that the goal of human parity would never be realized, Microsoft notes. It’s surprising, then, how quickly the researchers were able to achieve this milestone – especially given that machine translation is a problem people have been trying to solve for decades. The sample set, called newstest2017, was released just last fall at the research conference WMT17. The company says it’s tested the system repeatedly on a sample of around 2,000 sentences from various online newspapers, comparing the result to a person’s translation in the process – and even hiring outside bilingual language consultants to further verify the machine’s accuracy. A team of Microsoft researchers announced on Wednesday they’ve created the first machine translation system that’s capable of translating news articles from Chinese to English with the same accuracy as a person.
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