Iterative Alternating Neural Attention for Machine Reading
  We propose a novel neural attention architecture to tackle machine comprehension tasks, such as answering Cloze-style queries with respect to a document. Unlike previous models, we do not collapse the query into a single vector, instead we deploy an iterative alternating attention mechanism that allows a fine-grained exploration of both the query and the document. Our model outperforms state-of-the-art baselines in standard machine comprehension benchmarks such as CNN news articles and the Children's Book Test (CBT) dataset. 
Alessandro Sordoni and Philip Bachman and Adam Trischler and Yoshua   Bengio
cs.CL cs.NE