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

ICLR 2020 Yuxian MengXiangyuan RenZijun SunXiaoya LiArianna YuanFei WuJiwei Li

In this paper, we investigate the problem of training neural machine translation (NMT) systems with a dataset of more than 40 billion bilingual sentence pairs, which is larger than the largest dataset to date by orders of magnitude. Unprecedented challenges emerge in this situation compared to previous NMT work, including severe noise in the data and prohibitively long training time... (read more)

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