Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation

In this setting, an NMT sys- tem is firstly trained using auxiliary parallel data from a so-called “parent” language pair and then the trained model is used to initialize a

Statistical and neural MT systems were built, trained on the same in-domain literary data, and six participants with experience of literary translation tried translating sections