  In Neural Machine Translation (NMT), data augmentation methods such as
back-translation have proven their effectiveness in improving translation
performance. In this paper, we propose a novel data augmentation approach for
NMT, which is independent of any additional training data. Our approach, AdMix,
consists of two parts: 1) introduce faint discrete noise (word replacement,
word dropping, word swapping) into the original sentence pairs to form
augmented samples; 2) generate new synthetic training data by softly mixing the
augmented samples with their original samples in training corpus. Experiments
on three translation datasets of different scales show that AdMix achieves
signifi cant improvements (1.0 to 2.7 BLEU points) over strong Transformer
baseline. When combined with other data augmentation techniques (e.g.,
back-translation), our approach can obtain further improvements.
