Reasoning about Entailment with Neural Attention

ICLR 2016

Automatically recognizing entailment relations between pairs of natural language sentences has so far been the dominion of classifiers employing hand engineered features derived from natural language processing pipelines. End-to-end differentiable neural architectures have failed to approach state-of-the-art performance until very recently. In this paper, we propose a neural model that reads two sentences to determine entailment using long short-term memory units. We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases. Furthermore, we present a qualitative analysis of attention weights produced by this model, demonstrating such reasoning capabilities. On a large entailment dataset this model outperforms the previous best neural model and a classifier with engineered features by a substantial margin. It is the first generic end-to-end differentiable system that achieves state-of-the-art accuracy on a textual entailment dataset.

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