Learning how to actively learn: a deep imitation learning approach

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    3 Citations (Scopus)

    Abstract

    Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. We introduce a method
    that learns an AL policy using imitation learning (IL). Our IL-based approach
    makes use of an efficient and effective algorithmic expert, which provides the
    policy learner with good actions in the encountered AL situations. The AL strategy is then learned with a feedforward network, mapping situations to most
    informative query datapoints. We evaluate our method on two different tasks: text classification and named entity recognition. Experimental results show that our IL-based AL strategy is more effective than strong previous methods using
    heuristics and reinforcement learning.
    Original languageEnglish
    Title of host publicationACL 2018 - The 56th Annual Meeting of the Association for Computational Linguistics
    Subtitle of host publicationProceedings of the Conference, Vol. 1 (Long Papers)
    EditorsIryna Gurevych, Yusuke Miyao
    Place of PublicationStroudsburg PA USA
    PublisherAssociation for Computational Linguistics (ACL)
    Pages1874-1883
    Number of pages10
    ISBN (Print)9781948087322
    Publication statusPublished - 2018
    EventAnnual Meeting of the Association for Computational Linguistics 2018 - Melbourne, Australia
    Duration: 15 Jul 201820 Jul 2018
    Conference number: 56th
    https://aclanthology.info/events/acl-2018

    Conference

    ConferenceAnnual Meeting of the Association for Computational Linguistics 2018
    Abbreviated titleACL 2018
    CountryAustralia
    CityMelbourne
    Period15/07/1820/07/18
    Internet address

    Cite this

    Liu, M., Buntine, W., & Haffari, G. (2018). Learning how to actively learn: a deep imitation learning approach. In I. Gurevych, & Y. Miyao (Eds.), ACL 2018 - The 56th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 1 (Long Papers) (pp. 1874-1883). Stroudsburg PA USA: Association for Computational Linguistics (ACL).