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        <title>On the Frailty of Universal POS Tags for Neural UD Parsers</title>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2020.conll-1.6</identifier>
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%0 Conference Proceedings
%T On the Frailty of Universal POS Tags for Neural UD Parsers
%A Anderson, Mark
%A Gómez-Rodríguez, Carlos
%Y Fernández, Raquel
%Y Linzen, Tal
%S Proceedings of the 24th Conference on Computational Natural Language Learning
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F anderson-gomez-rodriguez-2020-frailty
%X 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.
%R 10.18653/v1/2020.conll-1.6
%U https://aclanthology.org/2020.conll-1.6/
%U https://doi.org/10.18653/v1/2020.conll-1.6
%P 69-96

Markdown (Informal)

[On the Frailty of Universal POS Tags for Neural UD Parsers](https://aclanthology.org/2020.conll-1.6/) (Anderson & Gómez-Rodríguez, CoNLL 2020)

• On the Frailty of Universal POS Tags for Neural UD Parsers (Anderson & Gómez-Rodríguez, CoNLL 2020)

ACL

• Mark Anderson and Carlos Gómez-Rodríguez. 2020. On the Frailty of Universal POS Tags for Neural UD Parsers. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 69–96, Online. Association for Computational Linguistics.