A Decomposable Attention Model for Natural Language Inference

Understand

We propose a simple neural architecture for natural language inference.

• Our approach uses attention to decompose the problem into subproblems 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.