Understanding Back-Translation at Scale

An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences. We find that in all but resource poor settings back-translations obtained via sampling or noised beam outputs are most effective. Our analysis shows that sampling or noisy synthetic data gives a much stronger training signal than data generated by beam or greedy search. We also compare how synthetic data compares to genuine bitext and study various domain effects. Finally, we scale to hundreds of millions of monolingual sentences and achieve a new state of the art of 35 BLEU on the WMT'14 English-German test set.

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Results from the Paper

Ranked #2 on Machine Translation on WMT2014 English-German (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Machine Translation WMT2014 English-French Noisy back-translation BLEU score 45.6 # 2
SacreBLEU 43.8 # 2
Hardware Burden 180G # 1
Operations per network pass None # 1
Machine Translation WMT2014 English-German Noisy back-translation BLEU score 35.0 # 2
SacreBLEU 33.8 # 1
Hardware Burden 146G # 1
Operations per network pass None # 1


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