On the Frailty of Universal POS Tags for Neural UD Parsers

Abstract

We present an analysis on the effect UPOS accuracy has on parsing performance. Results suggest that leveraging UPOS tags as features for neural parsers requires a prohibitively high tagging accuracy and that the use of gold tags offers a non-linear increase in performance, suggesting some sort of exceptionality. We also investigate what aspects of predicted UPOS tags impact parsing accuracy the most, highlighting some potentially meaningful linguistic facets of the problem.

To be published in proceedings of the 24th SIGNLL Conference on Computational Natural Language Learning (CoNLL). Be aware of long appendix: please don't print all 28 pages