Neural machine translation (NMT) (Bahdanau et al., 2014;Sennrich et al., 2016a;Wang et al., 2017b) is now the state-of-the-art in machine translation, due to its ability to be trained end-toend on large parallel corpora and capture complex parameterized functions that generalize across a variety of syntactic and semantic phenomena.
it has also been noted that compared to alternatives such as phrase-based translation (Koehn et al., 2003), NMT has trouble with lowfrequency words or phrases (Arthur et al., 2016;Kaiser et al., 2017), and also generalizing across domains (Koehn and Knowles, 2017).