Recurrent Graph Syntax Encoder for Neural Machine Translation
Table 3: Comparing with several SOTA models on WMT14 En-De test sets. “↑ / ⇑”: significantly outperform their counterpart (p<0.05/0.01).
['[BOLD] System', 'BLEU', '#para.']
In addition, we also conducted experiments on WMT14 En-De dataset to assess our model performance compared with several state-of-the-art systems. Tab. ; Gehring et al. Vaswani et al. ; Shaw et al. Yang et al. For fairly comparing with existing syntax incorporating method Bastings et al. Transformer, BiRNN+GCN Transformer in lower layers ([1-3]). Notably, RGSE-based Transformer-big model surpasses several existing powerful models, and even achieves the competitive result compared to the most advanced DynamConv model Wu et al.