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.- Anthology ID:
- N18-2038
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 236–241
- Language:
- URL:
- https://aclanthology.org/N18-2038
- DOI:
- 10.18653/v1/N18-2038
- Cite (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.
- Cite (Informal):
- Sluice Resolution without Hand-Crafted Features over Brittle Syntax Trees (Rønning et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/N18-2038.pdf