Bidirectional Attention Flow for Machine Comprehension

Understand

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.