Besides, our models outperform all the existing works, indicating the superiority of the proposed approaches.
cSANs achieve better performance than Parameter Speed BLEU TRANSFORMER-BASE (Vaswani et al., 2017) 88.0M 1.28 27.31 -+ BI DIREcT (Shen et al., 2018a) +0.0M -0.00 27.58 +0.27 + REL POS (Shaw et al., 2018) +0.1M -0.11 27.63 +0.32 + NEIGHBOR (Sperber et al., 2018) +0.4M -0.06 27.60 +0.29 + LOcAL HARD (Luong et al., 2015) +0.4M -0.06 27.73 +0.42 + LOcAL SOFT  +0.8M -0.09 27.81 +0.50 + BLOcK (Shen et al., 2018b) +6.0M -0.33 27.59 +0.28 + cNNs (Yu et al., 2018) +42.6M -0.54 27.70 +0.39 + 1D-cSANs +0.0M -0.00 27.86 +0.55 + 2D-cSANs +0.0M -0.06 28.18 +0.87 Table 1: comparing with the existing approaches on WMT14 En⇒De translation task.