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In this paper, we investigate the problem of training neural machinetranslation (NMT) systems with a dataset of more than 40 billion bilingualsentence pairs, which is larger than the largest dataset to date by orders ofmagnitude. Unprecedented challenges emerge in this situation compared toprevious NMT work, including severe noise in the data and prohibitively longtraining time. We propose practical solutions to handle these issues anddemonstrate that large-scale pretraining significantly improves NMTperformance. We are able to push the BLEU score of WMT17 Chinese-Englishdataset to 32.3, with a significant performance boost of +3.2 over existingstate-of-the-art results.

Large-scale Pretraining for Neural Machine Translation with Tens of Billions of Sentence Pairs