With the rise of NLP and ML, we've seen much progress in regards to the task of machine comprehension and building robust question answering systems. we want to focus on investigating and improving the BiDAF model, starting from extending the baseline model by including character-level word embeddings. We then ran experiments using the improvements recommended in section 5.11 of the default project handout. Two major goals were accomplished: we implemented character-level embeddings and adjusted dropout rate and learning rate in addition to other hyper-parameters in order to improve our model. On our best model, we were able to achieve an F1 score of 65.106 and a EM score of 61.369 in the non-PCE division.
