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 this https URL

通过使用多个前向和反向模型的预测结果来实现数据多样化， Data Diversification 是一种简单但有效的策略来提高神经机器翻译性能，它在不增加计算量和参数的情况下，适用于所有 NMT 模型，并在 WMT'14 英语 - 德语和英语 - 法语翻译任务中突破了最新的 BLEU 得分，并在 8 个其他翻译任务中取得了显著进展，它比知识蒸馏和双向学习更有效，并以牺牲复杂度换取更好的 BLEU 得分.