Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees

Ola Rønning; Daniel Hardt; Anders Søgaard

Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees

Ola Rønning, Daniel Hardt, Anders Søgaard

Institut for Digitalisering

Københavns Universitet

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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Abstract

Sluice resolution in English is the problem of finding antecedents of wh-fronted ellipses. Previous work has relied on handcrafted 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.

Originalsprog	Engelsk
Titel	Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies
Redaktører	Stephanie Lukin, Margaret Mitchell
Antal sider	6
Vol/bind	2
Udgivelsessted	New Orleans, LA
Forlag	Association for Computational Linguistics
Publikationsdato	2018
Sider	236–241
ISBN (Trykt)	9781948087292
Status	Udgivet - 2018
Begivenhed	The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - New Orleans, USA Varighed: 1 jun. 2018 → 6 jun. 2018 Konferencens nummer: 16 http://naacl2018.org/

Konference

Konference	The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics
Nummer	16
Land/Område	USA
By	New Orleans
Periode	01/06/2018 → 06/06/2018
Internetadresse	http://naacl2018.org/

Adgang til dokumentet

N18-2038Forlagets udgivne version, 165 KBLicens: CC BY-NC-ND

http://aclweb.org/anthology/N18-2Licens: Ikke-specificeret

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Citationsformater

Rønning, O., Hardt, D., & Søgaard, A. (2018). Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees. I S. Lukin, & M. Mitchell (red.), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Bind 2, s. 236–241). Association for Computational Linguistics. http://aclweb.org/anthology/N18-2

Rønning, Ola ; Hardt, Daniel ; Søgaard, Anders. / Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. red. / Stephanie Lukin ; Margaret Mitchell. Bind 2 New Orleans, LA : Association for Computational Linguistics, 2018. s. 236–241

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title = "Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees",

abstract = "Sluice resolution in English is the problem of finding antecedents of wh-fronted ellipses. Previous work has relied on handcrafted 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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editor = "Stephanie Lukin and Margaret Mitchell",

booktitle = "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics",

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Rønning, O, Hardt, D & Søgaard, A 2018, Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees. i S Lukin & M Mitchell (red), Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. bind 2, Association for Computational Linguistics, New Orleans, LA, s. 236–241, The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics, New Orleans, Louisiana, USA, 01/06/2018. <http://aclweb.org/anthology/N18-2>

Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees. / Rønning, Ola; Hardt, Daniel; Søgaard, Anders.
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. red. / Stephanie Lukin; Margaret Mitchell. Bind 2 New Orleans, LA: Association for Computational Linguistics, 2018. s. 236–241.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

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T1 - Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees

AU - Rønning, Ola

AU - Hardt, Daniel

AU - Søgaard, Anders

N1 - Conference code: 16

PY - 2018

Y1 - 2018

N2 - Sluice resolution in English is the problem of finding antecedents of wh-fronted ellipses. Previous work has relied on handcrafted 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.

AB - Sluice resolution in English is the problem of finding antecedents of wh-fronted ellipses. Previous work has relied on handcrafted 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.

M3 - Article in proceedings

SN - 9781948087292

VL - 2

SP - 236

EP - 241

BT - Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics

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T2 - The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics

Y2 - 1 June 2018 through 6 June 2018

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