(2015)) now represents the state-of-the-art adapted in most machine translation systems (Wu et al., 2016;Crego et al., 2016;Bojar et al., 2017a), largely due to its ability to benefit from end-to-end training on massive amounts of data.
recentlyintroduced self-attentional Transformer architectures (Vaswani et al., 2017) are rapidly becoming the de-facto standard in NMT, having demonstrated both superior performance and training speed compared to previous architectures using recurrent neural networks (RNNs; (Kalchbrenner and Blunsom, 2013;Sutskever et al., 2014)).