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Natural Language Inference on SNLI hard 1.0 (test)

84.48AccuracyBERT-base + PoE w/ Seq-Z
54.78862.496570.20577.9135Mar 24, 2022Jul 1, 2022Oct 8, 2022Jan 15, 2023Apr 24, 2023Aug 1, 2023Nov 8, 2023

Evaluation results

MethodLinks
BERT-base + PoE w/ Seq-ZBackbone=BERT-base, Debiasing Strategy=PoE, Training Data=Seq-Z Z(DG+ -> Z(DSNLI))2022.0384.48
DFLTraining Data=SNLI, Debiasing Strategy=Debiased Focal Loss2022.0383.01
BERT-base + PoE w/ DSNLIBackbone=BERT-base, Debiasing Strategy=PoE, Training Data=DSNLI2022.0382.92
BERT-base w/ Seq-ZBackbone=BERT-base, Debiasing Dataset=Seq-Z Z(DG -> Z(DSNLI))2022.0382.82
BERT-base w/ Par-ZBackbone=BERT-base, Debiasing Dataset=Par-Z Z(DSNLI) U Z(DG+)2022.0382.81
PoETraining Data=SNLI, Debiasing Strategy=Product of Experts2022.0382.15
BERT-base w/ Z-AugBackbone=BERT-base, Debiasing Dataset=Z-Aug Z(DG + DSNLI)2022.0381.78
BERT-base baselineBackbone=BERT-base, Training Data=DSNLI2022.0380.34
BERT teacherRole=Teacher, Architecture=BERT2023.0580.31
CRvNNModel Type=Sequence Encoder2023.1170.6
OMModel Type=Sequence Encoder2023.1170.6
BT-GRCModel Type=Sequence Encoder2023.1170.5
RIR-EBT-GRCModel Type=Sequence Encoder2023.1170.4
BT-GRC OSModel Type=Sequence Encoder2023.1170.3
EBT-GRCModel Type=Sequence Encoder2023.1169.9
RIR-GRCModel Type=Sequence Encoder2023.1169.6
BBT-GRCModel Type=Sequence Encoder2023.1169.2
Ens. AdvClsTraining Data=SNLI, Debiasing Strategy=Ensemble Adversarial Classifier2022.0367.42
AdvClsTraining Data=SNLI, Debiasing Strategy=Adversarial Classifier2022.0366.27
DTA with DMURole=Student, Architecture=TinyBERT2023.0566.04
DMURole=Student, Architecture=TinyBERT2023.0565.94
DTARole=Student, Architecture=TinyBERT2023.0560.26
KD + SmoothingRole=Student, Architecture=TinyBERT2023.0560.07
KD (standard distillation)Role=Student, Architecture=TinyBERT2023.0560.02
TinyBERT baselineRole=Student, Architecture=TinyBERT2023.0556.98
Baseline w/ labelled aug. dataRole=Student, Architecture=TinyBERT2023.0556.73
JTTRole=Student, Architecture=TinyBERT2023.0555.93