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 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.

Extracted Key Phrases

3 Figures and Tables

05020162017
Citations per Year

75 Citations

Semantic Scholar estimates that this publication has 75 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Parikh2016ADA, title={A Decomposable Attention Model for Natural Language Inference}, author={Ankur P. Parikh and Oscar T{\"a}ckstr{\"{o}m and Dipanjan Das and Jakob Uszkoreit}, booktitle={EMNLP}, year={2016} }