Iterative Alternating Neural Attention for Machine Reading

TL;DR

This paper introduces an iterative alternating neural attention model for machine reading comprehension that explores both query and document in detail, outperforming previous models on standard benchmarks.

Contribution

It presents a novel neural attention architecture that iteratively attends to query and document separately, enhancing comprehension capabilities.

Findings

Outperforms state-of-the-art baselines on CNN and CBT datasets.

Demonstrates the effectiveness of iterative alternating attention in machine comprehension.

Achieves significant improvements in answer accuracy.

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.