> It's not brain-dead, it's a result of limited training data. That's quite literally a cheap excuse. It'snot lack of training data (remember, you still need t

> It's not brain-dead, it's a result of limited training data. That's quite literally a cheap excuse. It'snot lack of training data (remember, you still need translations in the target language anyway!), it's a result of Anglo-Saxon cultural hegemony. > There are far fewer pieces of writing that exist in both German and Russian than in both German and English or both Russian and English. Right. And that's why Russia-based Yandex gets excellent results for German/Russian translation, I see... > But you can be sure that if it produced reliably better results with the data available, the likes of Google would already be using it. Nah, you're being way too optimistic there. Google is a business and as such, they don't chase SOTA outside of research (which doubles as advertisement). Good enough is the name of the game and it's simply infinitely cheaper to just train and maintain N-models (i.e. English/X and X/English) than to do the same for all combinations of X/Y-pairs (which would be N² models). So as long as there is no incentive to do better, they simply won't, because they rely on the fact that English is the de-facto Lingua Franca and people translate to and from English more often than directly between other languages. This would be an actual chance for companies from other countries (especially European ones) to step in and do exactly that, but again - "good enough" and reliance on English stops that from happening anytime soon. It's a missed opportunity especially with low-resource languages as demonstrated by the recent "Scottish" Wikipedia fiasco...