论文标题
通过能力估计，学习课程学习的动态数据选择
Dynamic Data Selection for Curriculum Learning via Ability Estimation
论文作者
论文摘要
课程学习方法通常依靠启发式方法来估计训练示例或模型能力的难度。在这项工作中，我们建议用学习的困难参数代替启发式方法。我们还建议通过能力估计（DDACLAE）进行课程学习的动态数据选择，该策略在每个训练时期探究模型能力，以选择当时的最佳训练示例。我们表明，在胶水分类任务上使用学习难度和/或能力的模型优于基于启发式的课程学习模型。
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.
