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://aclanthology.org/P18-2008
- DOI:
- 10.18653/v1/P18-2008
- Cite (ACL):
- Long Duong, Hadi Afshar, Dominique Estival, Glen Pink, Philip Cohen, and Mark Johnson. 2018. Active learning for deep semantic parsing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 43–48, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Active learning for deep semantic parsing (Duong et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/P18-2008.pdf