Neural Machine Translation is everywhere (and not just on this blog). Translators want to know how it will affect their livelihood, and internal localization managers want to know how they can make it work for their translation strategy. Whether you're looking to assess the business applications of neural machine translation, or peek under the hood to see how all the gears fit together, these NMT videos can help you wrap your head around the rising tide that is neural machine translation.
Machine Translation has historically been a dirty pair of words in localization. Experienced language professionals fear their own work, complete with nuanced diction and hyper sensitive geographical considerations, will be replaced by the cold, lifeless, robotic output of an algorithm. And considering recent approaches in translation, they’re not far off.
This post is an addendum to our original post on 1/10/2017 entitled 2017 Machine Translation Quality Evaluation. Experimental Design We evaluate all machine translation systems for English-French and English-German. We report case-insensitive BLEU-4 [2], which is computed by the mteval scoring script from the Stanford University open source toolkit Phrasal. NIST tokenization was applied to both the system outputs and the reference translations.
