Explicit Cross-lingual Pre-training for Unsupervised Machine Translation
Table 1: Comparison of the final unsupervised MT performance (BLEU). In this table, “Iter 2” means we do the whole process with another iteration as described in Section 3.4.
['[EMPTY]', 'Method', 'fr2en', 'en2fr', 'de2en', 'en2de', 'ro2en', 'en2ro']
The first two baselines Artetxe et al. ; Lample et al. The third baseline Artetxe et al. The fourth baseline Lample et al. The fifth baseline Ren et al. From the table, we find that our proposed method significantly outperforms previous methods on all language pairs by the average BLEU score of 1.7, and both the improvements of en2fr and ro2en are over 2 BLEU points. The results indicate that the explicit cross-lingual information incorporated by our proposed CMLM is beneficial to the unsupervised machine translation task. and ro2en bigger than 0.5 BLEU point, which confirms the potential that fine-tuned machine translation models contain more beneficial cross-lingual information than the initial n-gram translation tables, which can be used to enhance the pre-trained model iteratively.