A Decomposable Attention Model for Natural Language Inference

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... (read more)

PDF Abstract EMNLP 2016 PDF EMNLP 2016 Abstract

Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Natural Language Inference SNLI 200D decomposable attention model with intra-sentence attention % Test Accuracy 86.8 # 44
% Train Accuracy 90.5 # 42
Parameters 580k # 3
Natural Language Inference SNLI 200D decomposable attention model % Test Accuracy 86.3 # 52
% Train Accuracy 89.5 # 48
Parameters 380k # 3

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet