“Bidirectional Attention Flow for Machine Comprehension”, 2016-11-05 (; backlinks; similar):
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 unidirectional 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 a bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization.
Our experimental evaluations show that our model achieves state-of-the-art results in Stanford Question Answering Dataset (SQuAD) and CNN/Daily Mail cloze test.