  We present an error analysis of neural UPOS taggers to evaluate why using
gold standard tags has such a large positive contribution to parsing
performance while using predicted UPOS tags either harms performance or offers
a negligible improvement. We evaluate what neural dependency parsers implicitly
learn about word types and how this relates to the errors taggers make to
explain the minimal impact using predicted tags has on parsers. We also present
a short analysis on what contexts result in reductions in tagging performance.
We then mask UPOS tags based on errors made by taggers to tease away the
contribution of UPOS tags which taggers succeed and fail to classify correctly
and the impact of tagging errors.
