  We evaluate machine comprehension models' robustness to noise and adversarial
attacks by performing novel perturbations at the character, word, and sentence
level. We experiment with different amounts of perturbations to examine model
confidence and misclassification rate, and contrast model performance in
adversarial training with different embedding types on two benchmark datasets.
We demonstrate improving model performance with ensembling. Finally, we analyze
factors that effect model behavior under adversarial training and develop a
model to predict model errors during adversarial attacks.
