Our task for this project is to is to design a question-answering system for the SQuAD 2.0 dataset that improves upon the BiDAF baseline model. To do this, we experiment with QANet, a transformer-based architecture. We also reintroduce a character-level embeddings on top of the provided BiDAF model, as well as a self-attention layer. Our best QANet model achieved 61.47/64.81 EM/F1 scores on the test set.
