  The usefulness of part-of-speech tags for parsing has been heavily questioned
due to the success of word-contextualized parsers. Yet, most studies are
limited to coarse-grained tags and high quality written content; while we know
little about their influence when it comes to models in production that face
lexical errors. We expand these setups and design an adversarial attack to
verify if the use of morphological information by parsers: (i) contributes to
error propagation or (ii) if on the other hand it can play a role to correct
mistakes that word-only neural parsers make. The results on 14 diverse UD
treebanks show that under such attacks, for transition- and graph-based models
their use contributes to degrade the performance even faster, while for the
(lower-performing) sequence labeling parsers they are helpful. We also show
that if morphological tags were utopically robust against lexical
perturbations, they would be able to correct parsing mistakes.
