  In this paper, we address the question answering challenge with the SQuAD 2.0
dataset. We design a model architecture which leverages BERT's capability of
context-aware word embeddings and BiDAF's context interactive exploration
mechanism. By integrating these two state-of-the-art architectures, our system
tries to extract the contextual word representation at word and character
levels, for better comprehension of both question and context and their
correlations. We also propose our original joint posterior probability
predictor module and its associated loss functions. Our best model so far
obtains F1 score of 75.842% and EM score of 72.24% on the test PCE leaderboad.
