Building a Robust QA system using an Adversarially Trained Ensemble

Despite monumental progress in natural language understanding, QA systems trained on giant datasets are still vulnerable to domain transfer. Evidence shows that language models pick up on domain-specific features which hinders it from generalizing to other domains. In this project, we implore the use of adversarial networks to regularize the fine-tuning process which encourages the generator model to learn more meaningful representations of context and questions. We then construct an ensemble of these models based on each model's performance on specific subgroups of questions.