@inproceedings{wisniewski-etal-2018-automatically,
title = "Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles",
author = "Wisniewski, Guillaume and
Lacroix, Oph{\'e}lie and
Yvon, Fran{\c{c}}ois",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2064",
doi = "10.18653/v1/N18-2064",
pages = "401--406",
abstract = "This work introduces a new strategy to compare the numerous conventions that have been proposed over the years for expressing dependency structures and discover the one for which a parser will achieve the highest parsing performance. Instead of associating each sentence in the training set with a single gold reference we propose to consider a set of references encoding alternative syntactic representations. Training a parser with a dynamic oracle will then automatically select among all alternatives the reference that will be predicted with the highest accuracy. Experiments on the UD corpora show the validity of this approach.",
}
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%0 Conference Proceedings
%T Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles
%A Wisniewski, Guillaume
%A Lacroix, Ophélie
%A Yvon, François
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F wisniewski-etal-2018-automatically
%X This work introduces a new strategy to compare the numerous conventions that have been proposed over the years for expressing dependency structures and discover the one for which a parser will achieve the highest parsing performance. Instead of associating each sentence in the training set with a single gold reference we propose to consider a set of references encoding alternative syntactic representations. Training a parser with a dynamic oracle will then automatically select among all alternatives the reference that will be predicted with the highest accuracy. Experiments on the UD corpora show the validity of this approach.
%R 10.18653/v1/N18-2064
%U https://aclanthology.org/N18-2064
%U https://doi.org/10.18653/v1/N18-2064
%P 401-406
Markdown (Informal)
[Automatically Selecting the Best Dependency Annotation Design with Dynamic Oracles](https://aclanthology.org/N18-2064) (Wisniewski et al., NAACL 2018)
ACL