We show that an AddSentDiverse-based adversariallytrained model beats an AddSent-trained model across 3 different adversarial test sets, showing an average improvement of 24.22% in F1 score, demonstrating a general increase in robustness.
even with our diversified adversarial training data, the model is still not fully resilient to AddSent-style attacks, e.g., its antonymy-style semantic perturbations.