Active learning for deep semantic parsing

Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen, Mark Johnson


Abstract
Semantic parsing requires training data that is expensive and slow to collect. We apply active learning to both traditional and “overnight” data collection approaches. We show that it is possible to obtain good training hyperparameters from seed data which is only a small fraction of the full dataset. We show that uncertainty sampling based on least confidence score is competitive in traditional data collection but not applicable for overnight collection. We propose several active learning strategies for overnight data collection and show that different example selection strategies per domain perform best.
Anthology ID:
P18-2008
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
43–48
Language:
URL:
https://www.aclweb.org/anthology/P18-2008
DOI:
10.18653/v1/P18-2008
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-2008.pdf
Note:
 P18-2008.Notes.pdf
Poster:
 P18-2008.Poster.pdf