In this project, we produced a question answering system on SQuAD 2.0. To enhance the task performance, we explored two kinds of models. One is baseline BiDAF model, we modified the baseline by adding character embeddings and implementing Co-Attention layers. We conducted the experiments thoroughly to evaluate the effects of each component. The other is QANet, which is a Transformer-based model, only including convolutional and self-attention layers and free of RNN component. We implemented the model from scratch and got some results during the experiments. We found our best result is from the BiDAF-related model and achieved F1 score 64.96, EM score 61.70 in validation set and F1 score 64.712, EM score 60.997 in test set.
