eBay saw a significant increase in international commerce after improving its its translation software, new research has revealed.
The study, co-authored by MIT economist Erik Brynjolfsson, found that after eBay improved its automatic translation program with machine-learning technology in 2014, commerce increased by 10.9% among pairs of countries where people could use the new system.
“That’s a striking number. To have it be so clear in such a short amount of time really says a lot about the power of this technology,” said Brynjolfsson, the Schussel Family Professor of Management Science at the MIT Sloan School of Management.
Putting the results in perspective, he noted that physical distance is, by itself, also a significant barrier to global commerce. The 10.9% change generated by eBay’s new translation software increased trade by the same amount as “making the world 26% smaller, in terms of its impact on the goods that we studied,” Brynjolfsson said.
The new eBay Machine Translation (eMT) system is a proprietary machine-learning program that improved translation quality on eBay’s site, explained Peter Dizikes from the MIT news office. The system was initially focused on English-Spanish translations, to facilitate trade between the United States and Latin America.
Previously, eBay had used Bing Translator to translate the titles of objects for sale. By one evaluation measure, called the Human Acceptance Rate (HAR), in which three experts accept or reject translations, the eMT system increased the number of acceptable Spanish-language item titles on eBay from 82% to 90%.
The structure of the study enabled the researchers to say with confidence that the new eBay program, and not outside factors, directly generated the change in trade volume among affected countries, Brynjolfsson concluded.
Brynjolfsson wrote the paper, ‘Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform’, with Xiang Hui and Meng Liu, both assistant professors in the Olin Business School at Washington University in St. Louis. It was published in the December issue of Management Science.
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Tags: machine learning