R-AT: Regularized Adversarial Training for Natural Language Understanding
Shiwen Ni, Jiawen Li, Hung‐Yu Kao · 2022
Currently, adversarial training has become a popular and powerful regularization method in the natural language domain.In this paper, we Regularized Adversarial Training (R-AT) via dropout, which forces the output probability distributions of different sub-models generated by dropout to be consistent under the same adversarial samples.Specifically, we generate adversarial samples by perturbing the word embeddings.For each adversarial sample fed to the model, R-AT minimizes both the adversarial risk and the bidirectional KL-divergence between the adversarial output distributions of two sub-models sampled by dropout.Through extensive experiments on 13 public natural language understanding datasets, we found that R-AT has improvements for many models (e.g., rnn-based, cnn-based, and transformer-based models).For the GLUE benchmark, when R-AT is only applied to the fine-tuning stage, it is able to improve the overall test score of the BERT-base model from 78.3 to 79.6 and the RoBERTa-large model from 88.1 to 88.6.Theoretical analysis reveals that R-AT has potential gradient regularization during the training process.Furthermore, R-AT can reduce the inconsistency between training and testing of models with dropout 1 .