In this work, we take a step towards practical "universal" NMT -training massively multilingual models which support up to 102 languages and with up to one million examples per languagepair simultaneously.
we focus on training "English-centric" many-to-many models, in which the training data is composed of many language pairs that contain English either on the source side or the target side.