Neural machine translation (NMT) based on the encoder-decoder architecture becomes the new state-of-the-art due to distributed representation and end-to-end learning (Cho et al., 2014;Bahdanau et al., 2015;Junczys-Dowmunt et al., 2016;Gehring et al., 2017;Vaswani et al., 2017).
these results show that our method could rectify more words (51 vs. 70) with less deterioration (17 vs. 11) than Arthur(test).