Self-Supervised Neural Machine Translation
Dana Ruiter, Cristina España-Bonet, Josef van Genabith · 2019
We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations.This is done in a self-supervised way without parallel data, in such a way that both tasks enhance each other during training.The method is language independent, introduces no additional hyper-parameters, and achieves BLEU scores of 29.21 (en2f r) and 27.36 (f r2en) on new-stest2014 using English and French Wikipedia data for training.