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Dynamic Data Selection for Curriculum Learning via Ability Estimation.


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.

SUBMITTER: Lalor JP 

PROVIDER: S-EPMC7771727 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Dynamic Data Selection for Curriculum Learning via Ability Estimation.

Lalor John P JP   Yu Hong H  

Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing 20201101


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 outp  ...[more]

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