Adversarial Augmentation Policy Search for Domain and Cross-Lingual Generalization in Reading Comprehension
Table 8: Robustness of RoBERTaBASE trained on a subset of adversaries to unseen adversaries. Results (F1 score) are shown on SQuAD dev set (ASD=AddSentDiverse, PQ=PerturbQuestion, PA=PerturbAnswer, SQ=SQuAD).
['[BOLD] Adversary Attack', '[BOLD] Trained on  [BOLD] SQuAD', '[BOLD] Trained on  [BOLD] SQ+ASD']
Robustness to Unseen Adversaries: We train RoBERTaBASE on a subset of adversarial attacks and evaluate it on the adversaries that were not in the training set, to analyze robustness of the model to unseen adversaries. In the first set of experiments, we train RoBERTaBASE on SQuAD v2.0 augmented with the AddSentDiverse counterpart of each original QA sample. In the second set of experiments, we train RoBERTaBASE on SQuAD which has been augmented with an adversarial dataset of the same size as SQuAD and contains equal number of samples from AddSentDiverse, PerturbQuestion and PerturbAnswer. respectively, showing that the model is significantly robust to multiple distractors within the same context and it also gains some resilience to paraphrasing operations. Conversely, we see a drop in performance on InvalidateAnswer, showing that it is easier for the model to be distracted by adversaries when the original answer is removed from the context. In the second set of experiments, we see that the model is significantly more robust to combinatorial adversaries like AddSentDiverse+PerturbAnswer when trained on the adversaries AddSentDiverse and PerturbAnswer individually. We also see a decline in performance on InvalidateAnswer.