Our Analysts' Insights

A strong website that delivers a clear and compelling message, tailored to your specific audience can help drive sales. It must succeed in both its content and its technical structure. In June 2017, CSA Research examined 305 websites from language service providers in depth.

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.

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.

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.

The explosion of enthusiasm for MT that followed Google’s announcements about neural machine translation (NMT) last year shows no signs of abating.

In the last year, CSA Research has been covering a new paradigm for translator productivity, one we call “augmented translation.” The term comes from “augmented reality”: applications that overlay images of the world with relevant information.

Note: This blog post is not intended to provide legal advice. For clarification of your legal obligations and rights, please consult with qualified legal counsel.

For several years, the field of quality checking tools has been largely stagnant, with incremental updates to established tools. Recently, TAUS’ Dynamic Quality Framework (DQF) and the EU’s Multidimensional Quality Metrics (MQM) have set the stage for new developments in quality assessment methods thanks to their new methods and push for standardization. In this blog, we’ll review three new market entrants that are hoping to shake up this area.

Every new development in the field of machine translation (MT) is accompanied by a set of numbers that purport to show dramatic improvement in terms of quality, usually BLEU or METEOR scores. These measures use a scale from 0 to 100 to quantify how similar the MT output is to one or more human translations of the same source text based on a mechanical analysis of how many of the same words show up and how likely they are to appear in the same order.

Is your company looking for ways to increase translator and interpreter efficiency, boost motivation, increase your teams’ output, and focus team members on billable work? Is simple compensation not enough? If so, maybe you need to look beyond tools, processes, and word rates to make work seem less serious by introducing something missing from translation memory tools: fun.