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.
Original language | English |
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Title of host publication | Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies |
Editors | Stephanie Lukin, Margaret Mitchell |
Number of pages | 6 |
Volume | 2 |
Place of Publication | New Orleans, LA |
Publisher | Association for Computational Linguistics |
Publication date | 2018 |
Pages | 236–241 |
ISBN (Print) | 9781948087292 |
Publication status | Published - 2018 |
Event | The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - New Orleans, United States Duration: 1 Jun 2018 → 6 Jun 2018 Conference number: 16 http://naacl2018.org/ |
Conference
Conference | The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics |
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Number | 16 |
Country/Territory | United States |
City | New Orleans |
Period | 01/06/2018 → 06/06/2018 |
Internet address |