A Decomposable Attention Model for Natural Language Inference

Abstract

We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subprob-lems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.

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ABCNN: Attentionbased convolutional neural network for modeling sentence pairs

  • Yin
  • 2016
3 Excerpts

Semantic theory

  • Jerrold J Katz
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1 Excerpt

Koehn2009] Philipp Koehn. 2009. Statistical machine translation

05020162017
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