  Machine comprehension (MC), answering a query about a given context
paragraph, requires modeling complex interactions between the context and the
query. Recently, attention mechanisms have been successfully extended to MC.
Typically these methods use attention to focus on a small portion of the
context and summarize it with a fixed-size vector, couple attentions
temporally, and/or often form a uni-directional attention. In this paper we
introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage
hierarchical process that represents the context at different levels of
granularity and uses bi-directional attention flow mechanism to obtain a
query-aware context representation without early summarization. Our
experimental evaluations show that our model achieves the state-of-the-art
results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze
test.
