200D decomposable attention model

Reported on 2 benchmarks across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing2 results

Natural Language InferenceonSNLI
% Test Accuracy· 2016-06-06
86.3
best: 94.7 (UnitedSynT5 (3B))
A Decomposable Attention Model for Natural Language Inference arXiv:1606.01933
Natural Language InferenceonSNLI
% Train Accuracy· 2016-06-06
89.5
best: 99.7 (+ Unigram and bigram features)
A Decomposable Attention Model for Natural Language Inference arXiv:1606.01933