Modeling Past and Future for Neural Machine Translation
Table 5: Results of De-En and En-De “synthetic data” denotes additional 10M monolingual sentences, which is not used in our work.
['[BOLD] System', '[BOLD] Architecture', '[BOLD] De-En Dev', '[BOLD] De-En Test', '[BOLD] En-De Dev', '[BOLD] En-De Test']
We also evaluate our model on the WMT17 benchmarks for both De-En and En-De. Our proposed model improves the strong baseline on both De-En and En-De. This shows that our proposed model work well across different language pairs. \newciteDel2017: wmt17 and \newciteedinWMT17: arxiv obtain higher BLEU scores than our model, because they use additional large scaled synthetic data (about 10M) for training. It maybe unfair to compare our model to theirs directly.