Improving Neural Machine Translation Models with Monolingual DataThe research presented in this publication was conducted in cooperation with Samsung Electronics Polska sp. z o.o. - Samsung R&D Institute Poland.
Table 7: English→German translation performance (Bleu) on WMT training/test sets (newstest2014; newstest2015). Systems differ in how the synthetic training data is obtained. Ensembles of 4 models (unless specified otherwise).
['[EMPTY]', 'Bleu DE→EN', 'Bleu EN→DE', 'Bleu EN→DE']
The quality of the German→English back-translation differs substantially, with a difference of 6 Bleu on newstest2015. Regarding the English→German systems trained on the different synthetic corpora, we find that the 6 Bleu difference in back-translation quality leads to a 0.6–0.7 Bleu difference in translation quality. This is balanced by the fact that we can increase the speed of back-translation by trading off some quality, for instance by reducing beam size, and we leave it to future research to explore how much the amount of synthetic data affects translation quality.