Dynamic Data Selection for Curriculum Learning via Ability Estimation

John P. Lalor, Hong Yu


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.
Anthology ID:
2020.findings-emnlp.48
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Editors:
Trevor Cohn, Yulan He, Yang Liu
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
545–555
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.48
DOI:
10.18653/v1/2020.findings-emnlp.48
Bibkey:
Cite (ACL):
John P. Lalor and Hong Yu. 2020. Dynamic Data Selection for Curriculum Learning via Ability Estimation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 545–555, Online. Association for Computational Linguistics.
Cite (Informal):
Dynamic Data Selection for Curriculum Learning via Ability Estimation (Lalor & Yu, Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.48.pdf
Data
GLUEMRPCMultiNLIQNLISSTSST-2