Phrase-Based & Neural Unsupervised Machine Translation
Таблица 2: Comparison with previous approaches. BLEU score for different models on the en−fr and en−de language pairs. Just using the unsupervised phrase table, and without back-translation (PBSMT (Iter. 0)), the PBSMT outperforms previous approaches. Combining PBSMT with NMT gives the best results.
['Model', 'en-fr', 'fr-en', 'en-de', 'de-en']
We report large gains on all language pairs and directions. For instance, on the en→fr task, our unsupervised PBSMT obtains a BLEU score of 28.1, outperforming the previous best result by more than 11 BLEU points. Even on a more complex task like en→de, both PBSMT and NMT surpass the baseline score by more than 10 BLEU points. Even before iterative back-translation, the PBSMT model significantly outperforms previous approaches, and can be trained in a few minutes.