Improving QA Robustness through Modified Adversarial Training

We improved the domain generalizability of a DistilBert Question Answering (QA) model by implementing adversarial training. By putting a conventional QA model in competition with a discriminator, we were able to generate domain invariant features that improved the QA model's robustness. We augmented this strategy by retraining our model on all of our available datasets to gain the best performance. Our model performed better than the baseline with unseen out of domain datasets.