Double Path Networks for Sequence to Sequence Learning
Table 2: Performance of different systems on Nist Chinese-English translation task.
['[EMPTY]', 'Nist04', 'Nist05', 'Nist06', 'Nist08', 'Nist12']
These baselines are RNNSearchs with some well acknowledged techniques including: 1) Using multi-channel information into encoder (MC-NMT) Incorporating word reorder information into NMT (NMT+Distortion) An attention mechanism with a directed-syntax (SD-NMT) We can observe that our method surpasses SAN and CNN baseline by 0.74-0.99 and 0.46-2.96 BLEU score in different test sets. In addition, we also compare our results with some related works, and our model outperforms MC-NMT by 0.47-4.27 BLEU, NMT+Distortion by 0.74- 4.31 BLEU and SD-NMT by 1.44-4.38 BLEU in Nist test sets. These results on Nist Chinese-English dataset prove that our model can also achieve improvement in large dataset.