Improving Out-of-Domain Question Answering Performance with Adversarial Training

In this project, we aim to investigate the effectiveness of adversarial training on improving out-of-domain performance of question answering tasks. We show that finetuning a pretrained transformer with adversarial examples generated with Fast Gradient Method (FGM) using in-domain training data consistently improves the out-of-domain performance of the model. We also analyze the performance difference in terms of computation cost, memory cost and accuracy between a variety of hyperparameter configurations for adversarial training.