What Taggers Fail to Learn, Parsers Need the Most
Abstract
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.
Cite
@article{arxiv.2104.01083,
title = {What Taggers Fail to Learn, Parsers Need the Most},
author = {Mark Anderson and Carlos Gómez-Rodríguez},
journal= {arXiv preprint arXiv:2104.01083},
year = {2021}
}
Comments
Due to be published in the proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021). Previously rejected at the 2021 Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)