Reference Language based Unsupervised Neural Machine Translation
Table 1: Comparison of the proposed method with previous work (BLEU). Overall best results are shown in bold (all of them are better than the corresponding baselines at significance level p<0.01 Collins et al. (2005)), the best ones in each group are underlined. PBSMT + NMT: Lample et al. (2018b), XLM: Conneau and Lample (2019), MASS: Song et al. (2019). In the results of ro→zh and zh→ro, in the form x[y], x represent the result of in-domain test set, and y is the result of out-of-domain test set.
['[EMPTY]', '[ITALIC] en- [ITALIC] fr- [ITALIC] ro  [ITALIC] en→ro', '[ITALIC] en- [ITALIC] fr- [ITALIC] ro  [ITALIC] ro→en', '[ITALIC] en- [ITALIC] fr- [ITALIC] ro  [ITALIC] fr→ro', '[ITALIC] en- [ITALIC] fr- [ITALIC] ro  [ITALIC] ro→fr', '[ITALIC] en- [ITALIC] ro- [ITALIC] zh  [ITALIC] en→ro', '[ITALIC] en- [ITALIC] ro- [ITALIC] zh  [ITALIC] ro→en', '[ITALIC] en- [ITALIC] ro- [ITALIC] zh  [ITALIC] ro→zh', '[ITALIC] en- [ITALIC] ro- [ITALIC] zh  [ITALIC] zh→ro', '#']
This section examines the effectiveness of the proposed reference agreement approach. Judging from the results, our UNMT basically reproduces XLM results, and makes some improvements over the original (probably because of differences in data sampling). Thus, our approach offers a strong baseline performance. Compared with the current state-of-the-art method MASS Song et al. This is because MASS adopts the new masked sequence to sequence the pre-training method, and the improvement of our method is orthogonal to the pre-training improvement, which will not affect the evaluation of our method. Our approaches have also obtained consistent improvements over different domains, further verifying the effectiveness of the method.