@inproceedings{anderson-gomez-rodriguez-2020-frailty,
    title = "On the Frailty of Universal {POS} Tags for Neural {UD} Parsers",
    author = "Anderson, Mark  and
      G{\'o}mez-Rodr{\'i}guez, Carlos",
    editor = "Fern{\'a}ndez, Raquel  and
      Linzen, Tal",
    booktitle = "Proceedings of the 24th Conference on Computational Natural Language Learning",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.conll-1.6/",
    doi = "10.18653/v1/2020.conll-1.6",
    pages = "69--96",
    abstract = "We present an analysis on the effect UPOS accuracy has on parsing performance. Results suggest that leveraging UPOS tags as fea-tures 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."
}