Switching-Aligned-Words Data Augmentation for Neural Machine Translation

In neural machine translation (NMT), data augmentation methods such as back-translation make it possible to use extra monolingual data to help improve translation performance, while it needs extra training data and the in-domain monolingual data is not always available. In this paper, we present a novel data augmentation method for neural machine translation by using only the original training data without extra data. More accurately, we randomly replace words or mixup with their aligned alternatives in another language when training neural machine translation models. Since aligned word pairs appear in the same position of each other during training, it is helpful to form bilingual embeddings which are proved useful to provide a performance boost \citep{liu2019shared}. Experiments on both small and large scale datasets show that our method significantly outperforms the baseline models.