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).
the current NMT is a global model that maximizes the performance on the overall data and has problems in handling low-frequency words and ambiguous words 1 , we refer these words as troublesome words and define them in Section 3.1.