@inproceedings{qi-manning-2017-arc,
title = "Arc-swift: A Novel Transition System for Dependency Parsing",
author = "Qi, Peng and
Manning, Christopher D.",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-2018",
doi = "10.18653/v1/P17-2018",
pages = "110--117",
abstract = "Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7{--}7.6{\%} relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.",
}
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<abstract>Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7–7.6% relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.</abstract>
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%0 Conference Proceedings
%T Arc-swift: A Novel Transition System for Dependency Parsing
%A Qi, Peng
%A Manning, Christopher D.
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F qi-manning-2017-arc
%X Transition-based dependency parsers often need sequences of local shift and reduce operations to produce certain attachments. Correct individual decisions hence require global information about the sentence context and mistakes cause error propagation. This paper proposes a novel transition system, arc-swift, that enables direct attachments between tokens farther apart with a single transition. This allows the parser to leverage lexical information more directly in transition decisions. Hence, arc-swift can achieve significantly better performance with a very small beam size. Our parsers reduce error by 3.7–7.6% relative to those using existing transition systems on the Penn Treebank dependency parsing task and English Universal Dependencies.
%R 10.18653/v1/P17-2018
%U https://aclanthology.org/P17-2018
%U https://doi.org/10.18653/v1/P17-2018
%P 110-117
Markdown (Informal)
[Arc-swift: A Novel Transition System for Dependency Parsing](https://aclanthology.org/P17-2018) (Qi & Manning, ACL 2017)
ACL
- Peng Qi and Christopher D. Manning. 2017. Arc-swift: A Novel Transition System for Dependency Parsing. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 110–117, Vancouver, Canada. Association for Computational Linguistics.