Neural Machine Translation (NMT) has been a big success story in the deep learning revolution. It has grown out of academic labs to large-scale adoption in a short period of time. Recently, we at Google announced that we are now providing neural translations to our users. In this talk, I will briefly review the history of machine translation and explain our GNMT (Google's NMT) system. I will talk about our approach to Multilingual NMT which aims to translate between multiple languages at the same time. This opens many interesting avenues for further research. Most notably, it enables us to perform Zero-Shot translation - the ability to translate between languages the model has never seen before. Further analysis of this phenomenon hints at a presence of an interlingua - a language independent representation.
This is joint work with many members from the Google Brain and Google Translate teams.
Melvin Johnson received his M.S in Computer Science from Stanford University in 2015. At Stanford, he was part of Chris Manning's Natural Language Processing group where he worked on Knowledge Base extraction and related tasks. He is now at Google, where he works on research in Machine Translation and NLP.