TechGist05

Together with all the news of catastrophic hurricane activity in the US, that we are being bombarded with recently, we also are seeing stories of serious data security breaches of privileged information, even in the world of business translation. As always, the security and privacy of the data can only be as good as the data security practices and the sophistication of the technological implementations, and thus the knee-jerk response of pointing to “bad” MT technology per se, and assigning blame at MT use practices is not quite fair. It is indeed possible to make MT services available for public and/or corporate community uses safe and secure if you know what you are doing and are careful. It may seem obvious to some, but effective use of any technology requires both competence and skill, and we see too many cases of inept implementations that result in sub-optimal outcomes.   Generally, even professional translation work done entirely by humans requires source conte...

In all the discussion on MT that we hear, we do not often hear much about the post-editors and what could be done to enhance and improve the often negatively viewed  PEMT task.  Lucía Guerrero provides useful insights on her direct experience in improving the work experience for post-editors. Interestingly over the years I have noted that, strategies to improve the post-editor experience can often make mediocre MT engines viable, and  failure to do so can make good engines fail in fulfilling the business promise. I cannot really say much beyond what Lucía says here other than restate what she is saying in slightly different words. The keys to success seem to be: Build Trust by establishing transparent and fair compensation and forthright work related communication Develop ways to involve post-editors in the MT engine refinement and improvement process involvement Demonstrate that  the Feedback Cycle does in fact improve the work experience on an ongoing basis ======...

This is a guest post by Gábor Ugray, who drills into the possible rationale and potential logic of the recent DeepL NMT announcement. DeepL have provided very little detail on why, or even what they have done beyond providing some BLEU scores, so at this point, all of us can only speculate on the rationale and business outlook for DeepL with this limited information.  Yes, they do seem to have slightly better scoring MT engines, but many of us know that these scores can vary from month to month, and can often be completely disingenuous and misleading . Even the great "Do No Evil" Google is not immune to piling up the bullshit with MT scores . This is the way of the MT warrior, it seems, especially if you have Google lineage.  However, from my perspective the quality of the Linguee offering and track record sets this announcement apart. Here is somebody who apparently understands how to organize translation data with metadata on large scale. I imagine they already have some u...

This is a guest post by Ondřej Matuška, t he Sales & Marketing Manager of Lexical Computing , a company that develops a corpus and language data analysis product called Sketch Engine .  I was first made aware of Sketch Engine by Jost Zetzsche's newsletter (276th Edition of the Tool Box) a few weeks ago. As relatively clean text corpora proliferate and grow in data volume, it becomes necessary to use new kinds of tools to understand this huge volume of text data, which may or may not be under consideration for translation. These new tools help us to understand how to accurately profile the most prominent linguistic patterns in large collections of textual language data and extract useful knowledge from these new corpora to help in many translation related tasks. For those of us in the MT world, there have always been student-made (mostly by graduate students in NLP and computational linguistic programs )  tools that were used and needed to understand the corpus for better M...