TechGist05

This is a more fleshed-out version of a blog post by Pete Smith and Henry Anderson of the University of Texas at Arlington already published on SDL.com . They describe initial results from a research project they are conducting on MT system quality measurement and related issues.  MT quality measurement, like human translation quality measurement, has been a difficult and challenging subject for both the translation industry and for many MT researchers and systems developers as the most commonly used metric BLEU, is now quite widely understood to be of especially limited value with NMT systems.  Most of the other text-matching NLP scoring measures are just as suspect, and practitioners are reluctant to adopt them as they are either difficult to implement, or the interpretation pitfalls and nuances of these other measures are not well understood. They all can generate a numeric score based on various calculations of Precision and Recall that need to be interpreted with great c...

This is a ranking of the most popular posts of 2016 on this blog according to Blogger, based on traffic and/or reader comment activity. Popular does not necessarily mean the best, and I have seen in the past that some posts that may not initially resonate, have real staying power and continue to be read actively years after the original publishing date, even though they are not initially popular. We can see from these rankings, that Neural MT certainly was an attention grabber for 2016, (even though I think for the business translation industry, Adaptive MT is a bigger game changer ) and I look forward to seeing how NMT becomes more fitted to translation industry needs over the coming year . I know with some certainty that the posts by Juan Rowda and Silvio Picinini will be read actively through the coming year and on, because they are not just current news that fades quickly like the Google NMT critique, but rather they are carefully gathered best practice knowledge that is useful as...