Recent advances in pre-trained language models have improved the performance
for text classification tasks. However, little attention is paid to the
priority scheduling strategy on the samples during training. Humans acquire
knowledge gradually from easy to complex concepts, and the difficulty of the
same material can also vary significantly in different learning stages.
Inspired by this insights, we proposed a novel self-paced dynamic curriculum
learning (SPDCL) method for imbalanced text classification, which evaluates the
sample difficulty by both linguistic character and model capacity. Meanwhile,
rather than using static curriculum learning as in the existing research, our
SPDCL can reorder and resample training data by difficulty criterion with an
adaptive from easy to hard pace. The extensive experiments on several
classification tasks show the effectiveness of SPDCL strategy, especially for
the imbalanced dataset.

本文提出了一种新颖的自适应自我学习曲线方法，并使用两种不同的标准来评估样本的难度，通过从简单到困难的自适应速度重新排序和重采样训练数据。在多个分类任务上进行了广泛实验，证明了 SPDCL 的有效性，尤其是对于不平衡的数据集。