Our Analysts' Insights

In 2017, CSA Research conducted the first large-scale, pro-bono, survey-based research on gender issues in language services. With over 2,000 respondents, the findings provided objective and reliable information about how women and men involved in the industry perceive these issues. On the eve of the #metoo movement, the report kicked off industry-wide discussion on the role gender plays. It provided detailed – and surprising – results for North America and Europe.

In recent months, we have increasingly heard from enterprise localization groups that their executives are pushing for the adoption of neural machine translation (NMT), driven largely by a very successful public relations campaign from Google that has touted the very real improvements in NMT over the past two years. Unfortunately, some business leaders have seen media coverage and concluded that they no longer need language professionals and can simply replace translators with the “magic” of AI.

User-generated content (UGC) has garnered a lot of attention due to the challenges it poses for localization, such as an abundance of spelling errors, the extent to which its meaning depends on context, a lack of consistency, and time sensitivity. But even as enterprises and language service providers (LSPs) struggle to deal with it, another type of generated content has been quietly swelling into a looming tsunami: machine-generated content (MGC). Today, increasing quantities of content appear ...

I have an interest in 19th-century sporting newspapers and would like to have them in digital form. Unfortunately, these newspapers used almost microscopic fonts, the type was often heavily worn, and their paper has degraded over time. Despite these problems, humans usually don’t have any trouble reading them, but optical character recognition (OCR) is another matter.

Translation technology has reached an inflection point, swept up in the big trends affecting every industry: big data, cloud computing, and artificial intelligence (AI). Language platforms are being renovated or replaced as enterprise buyers and innovative language service providers seek to align their language systems with the rest of their technology stacks. Dated monolithic translation management systems (TMSes) are giving way to microservice- and cloud-based architectures, with machine learn...

Speech-enabled search allows people to access information much faster than they can through typing– particularly in languages such as Chinese, Hindi, and Japanese – and especially when fingers and eyes are occupied with driving or making equipment repairs.Customer expectations are already shifting as people take for granted the ability to give voice instructions to Alexa, Cortana, Siri, and their local equivalents Why shouldn’t they be able to do the same as they place product orders through ...

Intel founder and former CEO Andy Grove wrote that, “Measurement against a standard makes you think through WHY the results were what they were.” Business-savvy organizations live by this dictum. They monitor various datapoints and develop key performance indicators (KPIs) – assessable values that show whether a company is meeting its strategic business objectives.

In addition to weighty issues related to privacy, data security, and transparency, global content is what brought Facebook CEO Mark Zuckerberg face-to-face with 44 members of the U.S. Senate this week. At the heart of the discussion was how the company plans to police harmful material while at the same time avoiding censorship.

The question – since the earliest days of the computerized language industry – has been whether translation companies are so different than other service business that they can’t use generalized software. The argument was that generalized applications, such as FileMaker or Microsoft Word, with vastly more engineers, features, and user communities, would prove more useful in the end than would industry-specific applications and business platforms with small R&D teams and limited feature sets. ...

Microsoft decided to up its public relations game around machine translation. A recent blog post trumpeted that the company’s neural machine translation (NMT) system has reached parity with human translators for Chinese-English news text.