TechGist05

This is a guest post by Christian Eisold from berns language consulting. For many who have been using MT seriously already, it is clear that efforts made to implement the use of the correct terminology are a very high-value effort, as this is an area that is also problematic with a purely human translation effort. In fact, I believe that there is evidence that suggests MT (when properly done) outputs much more consistent translations in terms of consistent terminology. In fact, in most expert discussions on the tuning of MT systems for superior performance, there is a very clear understanding that better output starts with work focused on ensuring terminological consistency and accuracy.  The way to enforce terminological consistency and accuracy has been well known to RBMT practitioners and is also well understood by expert SMT practitioners . Christian provides an overview of how this is done across different use scenarios and vendors below.  He also points to some of the ch...

This is an interesting guest post by Gábor Ugray on the potential of AI in the translation business.  We hear something about artificial intelligence almost every day now and are continually told that it will change our lives. AI is indeed helping to solve complex problems that even a year ago were virtually unthinkable. Mostly, these are problems where big data and massive computing can come together to produce new kinds of efficiencies and even production solutions. However, there are dangers and risks too, and it is wise to be aware of some of the basic driving forces that underly these problems. As we have seen with self-driving cars, sometimes things don't quite work as you would expect . These mishaps and unintended results can happen when we barely understand what and how the computer "understands". Machine learning is not perfect learning and much of what is learned through deep neural nets, in particular, is kind of mysterious, to put it nicely.  We have seen tha...

We see that Neural MT continues to build momentum and that already most people agree that generic NMT engines outperform generic phrase-based SMT engines. In fact, in recent DFKI research analysis, generic NMT even outperforms many domain-tuned Phrase-based SMT systems. Both Google and Microsoft now have an NMT foundation for many of their most actively used MT languages. However, in the professional use of MT where the MT engines are very carefully tuned and modified for a specific business purpose, PB-SMT is still the preferred model for now. It has taken many years, but today many practitioners understand the SMT technology better, and some even know how to use the various control levers available to tune an MT engine for their needs. Today, most customized PB-SMT systems involve building a series of models in addition to the basic translation memory derived translation model, to address various aspects of the automated translation process. Thus, some may also add a language model t...

This is a guest post by Kevin Hendzel whose previous post on The Translation Market was second only to the Post-editing Compensation post, in terms of long-term popularity and wide readership on this blog. This new post is reprinted with permission and is also available on Kevin's blog with more photos. I am always interested to hear different perspectives on issues that I look at regularly as I believe that is how learning happens. As with many interesting posts previously published, this I mistakenly thought started from some Twitter banter, when I presumed that Kevin saw this post, which sees MT driving translators (and the translation industry) into an Armageddon scenario. However, it turned out that Kevin wrote his post first, and the Armageddon post may have been a response to it. Steve presents a pretty grim outlook with not much hope in sight. Some of the comments on his post build on this gloomy and forbidding outlook and are worth a gander too. I myself felt that the...