Unsupervised Neural Machine Translation with Weight Sharing
Table 2: The translation performance on English-German, English-French and Chinese-to-English test sets. The results of Lample et al. (2017) are copied directly from their paper. We do not present the results of Artetxe et al. (2017b) since we use different training sets.
['[EMPTY]', 'en-de', 'de-en', 'en-fr', 'fr-en', 'zh-en']
As it can be seen, the proposed approach obtains significant improvements than the word-by-word baseline system, with at least +5.01 BLEU points in English-to-German translation and up to +13.37 BLEU points in English-to-French translation. This shows that the proposed model only trained with monolingual data effectively learns to use the context information and the internal structure of each language. Compared to the work of Lample et al. We believe that the unsupervised NMT is very promising. However, there is still a large room for improvement compared to the supervised upper bound. The gap between the supervised and unsupervised model is as large as 12.3-25.5 BLEU points depending on the language pair and translation direction.