A Data Cartography based MixUp for Pre-trained Language Models
Seo Yeon Park, Cornelia Caragea · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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 TD-MixUp, a novel MixUp strategy that leverages Training Dynamics and allows more informative samples to be combined for generating new data samples.Our proposed TD-MixUp 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.1