@inproceedings{ronning-etal-2018-sluice,
title = "Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees",
author = "R{\o}nning, Ola and
Hardt, Daniel and
S{\o}gaard, Anders",
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/N18-2038",
doi = "10.18653/v1/N18-2038",
pages = "236--241",
abstract = "Sluice resolution in English is the problem of finding antecedents of \textit{wh}-fronted ellipses. Previous work has relied on hand-crafted features over syntax trees that scale poorly to other languages and domains; in particular, to dialogue, which is one of the most interesting applications of sluice resolution. Syntactic information is arguably important for sluice resolution, but we show that multi-task learning with partial parsing as auxiliary tasks effectively closes the gap and buys us an additional 9{\%} error reduction over previous work. Since we are not directly relying on features from partial parsers, our system is more robust to domain shifts, giving a 26{\%} error reduction on embedded sluices in dialogue.",
}
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<abstract>Sluice resolution in English is the problem of finding antecedents of wh-fronted ellipses. Previous work has relied on hand-crafted features over syntax trees that scale poorly to other languages and domains; in particular, to dialogue, which is one of the most interesting applications of sluice resolution. Syntactic information is arguably important for sluice resolution, but we show that multi-task learning with partial parsing as auxiliary tasks effectively closes the gap and buys us an additional 9% error reduction over previous work. Since we are not directly relying on features from partial parsers, our system is more robust to domain shifts, giving a 26% error reduction on embedded sluices in dialogue.</abstract>
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%0 Conference Proceedings
%T Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees
%A Rønning, Ola
%A Hardt, Daniel
%A Søgaard, Anders
%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 ronning-etal-2018-sluice
%X Sluice resolution in English is the problem of finding antecedents of wh-fronted ellipses. Previous work has relied on hand-crafted features over syntax trees that scale poorly to other languages and domains; in particular, to dialogue, which is one of the most interesting applications of sluice resolution. Syntactic information is arguably important for sluice resolution, but we show that multi-task learning with partial parsing as auxiliary tasks effectively closes the gap and buys us an additional 9% error reduction over previous work. Since we are not directly relying on features from partial parsers, our system is more robust to domain shifts, giving a 26% error reduction on embedded sluices in dialogue.
%R 10.18653/v1/N18-2038
%U https://aclanthology.org/N18-2038
%U https://doi.org/10.18653/v1/N18-2038
%P 236-241
Markdown (Informal)
[Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees](https://aclanthology.org/N18-2038) (Rønning et al., NAACL 2018)
ACL
- Ola Rønning, Daniel Hardt, and Anders Søgaard. 2018. Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 236–241, New Orleans, Louisiana. Association for Computational Linguistics.