  MixUp is a data augmentation strategy where additional samples are generated
during training by combining random pairs of training samples and their labels.
However, selecting random pairs is not potentially an optimal choice. In this
work, we propose TDMixUp, a novel MixUp strategy that leverages Training
Dynamics and allows more informative samples to be combined for generating new
data samples. Our proposed TDMixUp first measures confidence, variability,
(Swayamdipta et al., 2020), and Area Under the Margin (AUM) (Pleiss et al.,
2020) to identify the characteristics of training samples (e.g., as
easy-to-learn or ambiguous samples), and then interpolates these characterized
samples. We empirically validate that our method not only achieves competitive
performance using a smaller subset of the training data compared with strong
baselines, but also yields lower expected calibration error on the pre-trained
language model, BERT, on both in-domain and out-of-domain settings in a wide
range of NLP tasks. We publicly release our code.
