Active learning for deep semantic parsing

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

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

11 Citations (Scopus)


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 languageEnglish
Title of host publicationACL 2018 - The 56th Annual Meeting of the Association for Computational Linguistics - Proceedings of the Conference, Vol. 2 (Short Papers) July 15
Subtitle of host publicationJuly 15 - 20, 2018 Melbourne, Australia
EditorsIryna Gurevych, Yusuke Miyao
Place of PublicationStroudsburg PA USA
PublisherAssociation for Computational Linguistics (ACL)
Number of pages6
ISBN (Electronic)9781948087346
Publication statusPublished - 2018
Externally publishedYes
EventAnnual Meeting of the Association of Computational Linguistics 2018 - Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018
Conference number: 56th


ConferenceAnnual Meeting of the Association of Computational Linguistics 2018
Abbreviated titleACL 2018
Internet address


  • semantic parsing
  • machine learning
  • Active learning

Cite this