Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks
This work addresses robustness issues in machine comprehension models, which is an incremental improvement for researchers and practitioners in natural language processing.
The paper tackles the problem of evaluating neural machine comprehension models' robustness to noisy inputs and adversarial attacks by performing perturbations at multiple levels, resulting in improved performance through ensembling and analysis of factors affecting model behavior.
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.