A number of methods have been proposed to ameliorate these problems, including methods that incorporate symbolic knowledge such as discrete translation lexicons (Arthur et al., 2016;He et al., 2016;Chatterjee et al., 2017) and phrase tables (Zhang et al., 2017;Tang et al., 2016;Dahlmann et al., 2017), adjust model structures to be more conducive to generalization (Nguyen and Chiang, 2017), or incorporate additional information about domain (Wang et al., 2017a) or topic  in translation models.
one paradigm of interest is recent work that augments NMT using retrieval-based models, retrieving sentence pairs from the training corpus that are most similar to the sentence that we want to translate, and then using these to bias the NMT model.