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arXiv · 1606.02245

Iterative Alternating Neural Attention for Machine Reading

Alessandro Sordoni·Philip Bachman·Adam Trischler·Yoshua Bengio
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Abstract

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.

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Alessandro Sordoni, Philip Bachman, Adam Trischler, Yoshua Bengio. 2016-06-07. Iterative Alternating Neural Attention for Machine Reading. https://arxiv.org/abs/1606.02245

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