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Microsoft Research Asia leads in machine translation competition

SINGAPORE: Microsoft Research Asia (MSRA) has achieved eight top places in the recent machine translation challenge organised by Fourth Conference on Machine Translation (WMT19), out of the 11 tasks it undertook. Overall, there are 19 machine translation categories in WMT this year.

MSRA achieved first place in machine translation tasks for Chinese-English, English-Finnish, English-German, English-Lithuanian, French-German, German-English, German-French and Russian-English. Three other tasks were placed second in their respective categories, which included English-Kazakh, Finnish-English and Lithuanian-English.

As one of the leading machine translation competition globally, WMT is a platform for leading researchers to demonstrate their solutions, as well as to understand the continuous evolvement of machine translation technology. Now in its 14th year, more than 50 teams globally from technology companies, leading academic institutions and universities participated in a bid to demonstrate their machine translation capabilities.

The organisers aimed to evaluate current machine translation techniques for the languages other than English, as well as to examine the challenges between European languages, including low resource and morphologically rich languages.

“This year, the MSRA team applied innovative algorithms to its system, which significantly improved the quality of the machine translation results. These algorithms were used to improve the platform’s learning mechanism, pre-training, network architecture optimisation, data enhancement and other processes required so that the system can perform better,” explained Tie-Yan Liu, Assistant Managing Director of MSRA.

The achievement follows the 2018 breakthrough whereby researchers in MSRA and Microsoft Research US labs reached human parity on a commonly used test set of news stories, called newstest2017, which was developed by a group of industry and academic partners and released at WMT17.

The system is able to translate a sentence of news articles from Chinese to English with the same quality and accuracy as a person.

“The realm of machine translation will continue to evolve with better algorithms, data set and technology. However, much of our research today is really inspired by how we humans do things,” Liu said. “Language is complex and nuanced, as people can use different words to express the exact same concept. Hence, developing multi-dimensional algorithms is important in evolving machine translation systems so that they can deliver better outcomes,” he explained.

“Our achievement at WMT19 serves to the further development of the field, whereby we hope that machine translation can become better in the years to come,” Liu remarked.

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