Google has a paper out on language translation at scale using deep neural nets. What is interesting about this paper is that the model architecture allows for training with a set of language pairs eg (english, german), ( french, italian), (english, chinese ) , (japanese, korean), (chinese, japanese ) and so on.. but while inferencing, we can also get the answers for an unseen pair for example give it a chinese text and a target language of german and get the translation. In a more traditional approach, we could 1) train separately for each pair or 2) train towards an intermediate representation (which could be english or a common language itself) . The advantage of this approach is that we can get the best out of all the available language pairs. The cool aspects of the paper are that we can get 0-shot learning for an unseen pair and there seems to be hints at an intermediate language being represented within the neural nets.
Wonder what the Skype universal translator does..
Will be great to see this for the set of Indian languages too!
here is the Google paper https://arxiv.org/pdf/1611.04558v1.pdf