In our 2011 report on trends in machine translation (MT), we found that "the statistics-based approach to MT is basically a big-data application" (see "Trends in Machine Translation," Oct11). We predicted that experts would apply these algorithms to crack inter-language communication and marketing issues as they processed more languages and huge volumes of multilingual content. We wrote that the use of such techniques would increase "both quality and understanding of how different languages affect perception and behavior."
This week SDL coined the term "big language" to describe the intersection of many languages and mushrooming content volumes. The company stated that these two forces have combined to transform "Big Data challenges into Big Language challenges." Besides introducing the term, the company announced that its Language Platform, which includes workflow and translation productivity solutions SDL WorldServer, TMS, and Studio, has been enhanced to support its BeGlobal machine translation software. It also added a self-training tool that will let in-house corporate and language service providers (LSP) teams improve MT quality and increase translator productivity.
Keith Laska, CEO of the SDL Language Technologies division said that "the Big Language message is resonating well" and hopes it will "create Big Language champions who can empower their organizations – both Enterprises and LSPs – to take more control of content explosion, and make MT a serious component of their post-editing and market expansion strategies."
Common Sense Advisory’s research has found that empowering in-house development is a long-term requirement to enable wider use of the technology – in the areas of training, quality, and usability (see "Developers Work to Broaden Use of Machine Translation," Mar10). This announcement caps several months of frenetic activity across the MT landscape, ranging from core products to supporting tools to LSPs actively investing in production models employing MT (see "Software Developers and LSPs Enhance Machine Translation Capabilities"). Big language represents both big challenges and big opportunities.
We just started a new research study on how enterprise (buy-side) organizations use machine translation. If you buy post-edited machine translation or MT software, please tell us about your experience. Even if you don’t use MT, our survey has some questions for you, too. And if you’re an LSP that uses MT or a software vendor that develops it, please brief us on your services or products.