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We propose a novel neural attention architecture to tackle machinecomprehension tasks, such as answering Cloze-style queries with respect to adocument. Unlike previous models, we do not collapse the query into a singlevector, instead we deploy an iterative alternating attention mechanism thatallows a fine-grained exploration of both the query and the document. Our modeloutperforms state-of-the-art baselines in standard machine comprehensionbenchmarks such as CNN news articles and the Children's Book Test (CBT)dataset.

Iterative Alternating Neural Attention for Machine Reading