  While most approaches to automatically recognizing entailment relations have
used classifiers employing hand engineered features derived from complex
natural language processing pipelines, in practice their performance has been
only slightly better than bag-of-word pair classifiers using only lexical
similarity. The only attempt so far to build an end-to-end differentiable
neural network for entailment failed to outperform such a simple similarity
classifier. 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.
