  The state of the art of handling rich morphology in neural machine
translation (NMT) is to break word forms into subword units, so that the
overall vocabulary size of these units fits the practical limits given by the
NMT model and GPU memory capacity. In this paper, we compare two common but
linguistically uninformed methods of subword construction (BPE and STE, the
method implemented in Tensor2Tensor toolkit) and two linguistically-motivated
methods: Morfessor and one novel method, based on a derivational dictionary.
Our experiments with German-to-Czech translation, both morphologically rich,
document that so far, the non-motivated methods perform better. Furthermore, we
iden- tify a critical difference between BPE and STE and show a simple pre-
processing step for BPE that considerably increases translation quality as
evaluated by automatic measures.
