We evaluate the efficacy of predicted UPOS tags as input features for dependency parsers in lower resource settings to evaluate how treebank size affects the impact tagging accuracy has on parsing performance. We do this for real low resource univers
al dependency treebanks, artificially low resource data with varying treebank sizes, and for very small treebanks with varying amounts of augmented data. We find that predicted UPOS tags are somewhat helpful for low resource treebanks, especially when fewer fully-annotated trees are available. We also find that this positive impact diminishes as the amount of data increases.
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.
