Question answering is a challenging problem that tests language processing models the ability to comprehend natural languages. In this project, we implemented two models, BiDAF and QANet, to solve the Stanford question answering dataset (SQuAD) 2.0. We experienced different methods to improve the performance of these models, including adding character embedding layers, data augmentation, and ensemble modeling. Finally, we compared the result across different experiments and gave an analysis of our models. In the end, our best model achieved F1/EM score of 68.71/65.38 in the test leaderboard.
