Combining SMT and NMT Back-Translated Data for Efficient NMT

Neural Machine Translation (NMT) models achieve their best performance when large sets of parallel data are used for training. Consequently, techniques for augmenting the training set have become popular recently. One of these methods is back-translation (Sennrich et al., 2016a), which consists on generating synthetic sentences by translating a set of monolingual, target-language sentences using a Machine Translation (MT) model.