Multi-encoder Transformer Network for Automatic Post-Editing
Jaehun Shin, Jong-Hyeok Lee · 2018
This paper describes the POSTECH's submission to the WMT 2018 shared task on Automatic Post-Editing (APE).We propose a new neural end-to-end post-editing model based on the transformer network.We modified the encoder-decoder attention to reflect the relation between the machine translation output, the source and the postedited translation in APE problem.Experiments on WMT17 English-German APE data set show an improvement in both TER and BLEU score over the best result of WMT17 APE shared task.Our primary submission achieves -4.52 TER and +6.81 BLEU score on PBSMT task and -0.13 TER and +0.40 BLEU score for NMT task compare to the baseline.