It ' s a blog post about an . To be fair to the blogpost , it does have a section about Evidence for Interlingua and does say some things that seem like the AI has some sort of internal model for representing languages . Once you show it how to translate a new language to any of the languages it knows , it can translate that language to any other language . And their model that can translate any language to any other is the same size as a normal 11 translation model . Here are the important bits . Summary In addition to improvingthe translation quality of language pairs that the model was trained with , our models can also learnto perform implicit bridging between language pairs never seen explicitly during training , showing thattransfer learning and zeroshot translation is possible for neural translation . ConclusionWe present a simple and elegant solution to multilingual NMT . We show that we can train multilingual NMTmodels that can be used to translate between a number of different languages using a single model where allparameters are shared , which as a positive sideeffect also improves the translation quality of lowresourcelanguages in the mix . We also show that zeroshot translation without explicit bridging is possible using thesemodels , which is the first time to our knowledge that a form of true transfer learning has been shown to workfor machine translation . To explicitly improve the zeroshot translation quality , we explore two different waysof adding available parallel data and find that small additional amounts are sufficient to reach satisfactoryresults . In our largest experiment we merge 12 language pairs into a single model of the same size as eachsingle language pair model , and achieve only slightly lower translation quality as for the single language pairbaselines despite the drastically reduced amount of modeling capacity per language in the multilingual model . Visual interpretation of the results shows that these models learn a form of interlingua representation for themultilingual model between all involved language pairs . The simple architecture makes it possible to mixlanguages on the source or target side to yield some interesting translation examples . Our approach has beenshown to work reliably in a Googlescale production setting and enables us to scale to a large number oflanguages quickly .