Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations
Table 5: Final results based on the transformer. Only the SAWR results are significantly better (p<0.05).
['System', 'MT03', 'MT04', 'MT05', 'MT06', 'Average/Δ']
As shown, the transformer results are indeed much better than RNN-based baseline. The BLEU scores show an average increase of 40.74−37.09=3.65. In addition, we can see that syntax information can still give positive influences based on the transformer. The SAWR approach can also outperform the baseline system significantly. Particularly, we find that our SAWR approach is much more effective than the Tree-RNN and Tree-Linearization approaches. The results further demonstrate the effectiveness of SAWRs in syntax integration for NMT.