  Post-editing (PE) machine translation (MT) is widely used for dissemination
because it leads to higher productivity than human translation from scratch
(HT). In addition, PE translations are found to be of equal or better quality
than HTs. However, most such studies measure quality solely as the number of
errors. We conduct a set of computational analyses in which we compare PE
against HT on three different datasets that cover five translation directions
with measures that address different translation universals and laws of
translation: simplification, normalisation and interference. We find out that
PEs are simpler and more normalised and have a higher degree of interference
from the source language than HTs.
