Papers › Iterative Alternating Neural Attention for Machine Reading
Iterative Alternating Neural Attention for Machine Reading
Alessandro Sordoni, Philip Bachman, Adam Trischler, Yoshua Bengio
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Question Answering | CNN / Daily Mail | AIA | CNN | 76.1 | #5 of 16 | Archive leaderboard | report |
| Question Answering | Children's Book Test | AIA | Accuracy-NE | 72% | #8 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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