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.
Original language | English |
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Title of host publication | ACL 2018 - The 56th Annual Meeting of the Association for Computational Linguistics - Proceedings of the Conference, Vol. 2 (Short Papers) July 15 |
Subtitle of host publication | July 15 - 20, 2018 Melbourne, Australia |
Editors | Iryna Gurevych, Yusuke Miyao |
Place of Publication | Stroudsburg PA USA |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 43-48 |
Number of pages | 6 |
Volume | 2 |
ISBN (Electronic) | 9781948087346 |
Publication status | Published - 2018 |
Externally published | Yes |
Event | Annual Meeting of the Association of Computational Linguistics 2018 - Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018 Conference number: 56th https://aclanthology.info/events/acl-2018 |
Conference
Conference | Annual Meeting of the Association of Computational Linguistics 2018 |
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Abbreviated title | ACL 2018 |
Country/Territory | Australia |
City | Melbourne |
Period | 15/07/18 → 20/07/18 |
Internet address |
Keywords
- semantic parsing
- machine learning
- Active learning