Word embedding layers encode input words into continuous low-dimension vectors, followed by positional encoding layers that add position information to them.
koehn and knowles (2017) report that even RNN-based model with the attention mechanism performs worse than phrase-based statistical machine translation (koehn et al., 2007) in translating very long sentences, which challenges us to develop an NMT model that is robust to long sentences or more generally, variations in input length.