Unsupervised Machine Translation Using Monolingual Corpora Only
Table 2: BLEU score on the Multi30k-Task1 and WMT datasets using greedy decoding.
['[EMPTY]', 'Multi30k-Task1 en-fr', 'Multi30k-Task1 fr-en', 'Multi30k-Task1 de-en', 'Multi30k-Task1 en-de', 'WMT en-fr', 'WMT fr-en', 'WMT de-en', 'WMT en-de']
First, we observe that word-by-word translation is surprisingly effective when translating into English, obtaining a BLEU score of 16.77 and 10.09 for fr-en on respectively Multi30k-Task1 and WMT datasets. Word-reordering only slightly improves upon word-by-word translation. Our model instead, clearly outperforms these baselines, even on the WMT dataset which has more diversity of topics and sentences with much more complicated structure. After just one iteration, we obtain a BLEU score of 27.48 and 12.10 for the en-fr task. Interestingly, we do even better than oracle reordering on some language pairs, suggesting that our model not only reorders but also correctly substitutes some words. After a few iterations, our model obtains BLEU of 32.76 and 15.05 on Multi30k-Task1 and WMT datasets for the English to French task, which is rather remarkable.