  Many downstream applications are using dependency trees, and are thus relying
on dependency parsers producing correct, or at least consistent, output.
However, dependency parsers are trained using machine learning, and are
therefore susceptible to unwanted inconsistencies due to biases in the training
data. This paper explores the effects of such biases in four languages -
English, Swedish, Russian, and Ukrainian - though an experiment where we study
the effect of replacing numerals in sentences. We show that such seemingly
insignificant changes in the input can cause large differences in the output,
and suggest that data augmentation can remedy the problems.
