We present an error analysis of neural UPOS taggers to evaluate why using gold tags has such a large positive contribution to parsing performance while using predicted UPOS either harms performance or offers a negligible improvement. We also 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 then mask UPOS tags based on errors made by taggers to tease away the contribution of UPOS tags that taggers succeed and fail to classify correctly and the impact of tagging errors.
