@inproceedings{lalor-yu-2020-dynamic,
    title = "Dynamic Data Selection for Curriculum Learning via Ability Estimation",
    author = "Lalor, John P.  and
      Yu, Hong",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.findings-emnlp.48/",
    doi = "10.18653/v1/2020.findings-emnlp.48",
    pages = "545--555",
    abstract = "Curriculum learning methods typically rely on heuristics to estimate the difficulty of training examples or the ability of the model. In this work, we propose replacing difficulty heuristics with learned difficulty parameters. We also propose Dynamic Data selection for Curriculum Learning via Ability Estimation (DDaCLAE), a strategy that probes model ability at each training epoch to select the best training examples at that point. We show that models using learned difficulty and/or ability outperform heuristic-based curriculum learning models on the GLUE classification tasks."
}