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.
PDF AbstractCode
Datasets
CNN/Daily Mail
CBT
Results from the Paper
Ranked #3 on
Question Answering
on Children's Book Test
(Accuracy-NE metric)
