Synchronous Bidirectional Neural Machine Translation
Table 3: Evaluation of translation quality for Chinese-English translation tasks using case-insensitive BLEU scores. All results of our model are significantly better than Transformer and Transformer (R2L) (p < 0.01).
['Model', 'DEV', 'MT03', 'MT04', 'M05', 'MT06', 'AVE', 'Δ']
Specifically, the proposed model significantly outperforms Moses, RNMT, Transformer, Transformer (R2L), Rerank-NMT and ABD-NMT by 13.23, 8.54, 3.92, 4.90, 2.91, 2.82 BLEU points, respectively. Compared to Transformer and Transformer (R2L), our model exhibits much better performance. These results confirm our hypothesis that the two directions are mutually beneficial in bidirectional decoding. Furthermore, compared to Rerank-NMT in which two decoders are relatively independent and ABD-NMT where only the forward decoder can rely on a backward decoder, our proposed model achieves substantial improvements over them on all test sets, which indicates that joint modeling and optimizing with left-to-right and right-to-left decoding behaves better in leveraging bidirectional decoding.