Being able to answer questions about a given passage marks a significant advancement in artificial intelligence. This task also has incredible practical utility, given the great need to have a personal assistant on our phones that can answer simple questions about world facts. In this project, we attempt to build a state-of-the-art model for question answering on the SQuAD 2.0 dataset via combining several different deep learning techniques. We iterated off of the baseline BiDAF model with various improvements such as feature engineering, character embeddings, co-attention, transformer models, and more. We had mixed success in getting all of these methodologies to fully run as anticipated and found many to not work as well as we had hoped. But we still managed to make significant improvements over the baseline by combining some of what we had implemented and performing a hyperparameter search. Our final model was quite successful on this front, achieving an F1 score of 63.517 and an EM score of 59.966 over the baseline's 58 F1 score and 55 EM score.
