For many years now, localization managers at enterprises have known how they should create content tailored for particular markets, even if they haven't actually done it. Instead, the model that evolved in the 1990s invested in optimizing existing content for translation.
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Based on press coverage of recent advances in machine translation (MT), it may seem as if all the major developments happen in the United States. However, since the 1980s, much of the basic research in this area has either happened in European projects or been headed by alumni of groups in the region such as the German Research Center for Artificial Intelligence (DFKI), University of Aachen, and the University of Edinburgh.
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The Indian e-commerce market continues to grow as the government there encourages its expansion and works to ensure that the country’s local languages are integrated into its Digital India and Make in India programs. Unlike its neighbor to the east – China – the Indian playing field is open to international companies, as the ongoing competition between Amazon and Flipkart and Uber and Ola Cabs demonstrates. Apple, Amazon, and Google have announced billions of dollars in additional investment ...
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One of the most frequent questions that CSA Research analysts are asked is how a translation team can help the rest of the company do a better job marketing to, selling to, and caring for local markets around the globe as well as multicultural ones in their home country. After all, it's usually not their mandated responsibility to support enterprise-wide business process globalization. However, if they don't take it on, they often find that they can't meet their own objectives for de...
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Big changes are afoot in cyberspace. In recent years, populations in Africa, Asia, and Oceania surged online with the aid of cheap data plans and government investments. In Myanmar, for example, the mobile grid went up in 2014 and a total population of 53 million now shows 33 million subscriptions, with 80% smartphone penetration and over 50% with active data plans. China initiatives, such as Belt and Road, Shanghai Cooperation Organization, and the Asia Infrastructure Investment Bank, are bring...
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It was the memo heard around the world, the one that led Google’s CEO to cut short his vacation to deal with a firestorm of criticism, and the one that reignited the perennial and fiery debate about sexism, women’s role in the tech industry, and political correctness.
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The explosion of enthusiasm for MT that followed Google’s announcements about neural machine translation (NMT) last year shows no signs of abating.
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Buyers of language services crave the ability to measure translation quality in an objective way, get easy-to-digest reports on how it is tracking over time, and be able to drill down as needed for process improvement. However, quality control remains a mostly human-driven process – even when supported by QA technology – because humans have to sift through the reports these tools produce. What if there was another way to approach quality?
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It has taken social and mobile – plus the empowerment of consumers to influence the message – for content to finally grab the undistracted attention of executives. But they’re not only wrestling with content in its simplest forms controlled by employees. They must also take into account material created “in the wild” – outside of their organizations – where it morphs into assorted flavors through local languages, dialects, and cultures.
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Two years ago, CSA Research determined that the language sector faced a perfect storm as industry players incrementally improved efficiency, innovated services and processes built on newer technology and streamlined practices, and confronted disruptive intrinsic and external changes that were transforming the market.
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