  We introduce Data Diversification: a simple but effective strategy to boost
neural machine translation (NMT) performance. It diversifies the training data
by using the predictions of multiple forward and backward models and then
merging them with the original dataset on which the final NMT model is trained.
Our method is applicable to all NMT models. It does not require extra
monolingual data like back-translation, nor does it add more computations and
parameters like ensembles of models. Our method achieves state-of-the-art BLEU
scores of 30.7 and 43.7 in the WMT'14 English-German and English-French
translation tasks, respectively. It also substantially improves on 8 other
translation tasks: 4 IWSLT tasks (English-German and English-French) and 4
low-resource translation tasks (English-Nepali and English-Sinhala). We
demonstrate that our method is more effective than knowledge distillation and
dual learning, it exhibits strong correlation with ensembles of models, and it
trades perplexity off for better BLEU score. We have released our source code
at https://github.com/nxphi47/data_diversification
