Sentence Classification on Stanford Sentiment Treebank (SST) fine-grained (test) benchmark leaderboard · SOTA2 Research

Sentence Classification on Stanford Sentiment Treebank (SST) fine-grained (test)

53.1AccuracyNTI-SLSTM-LSTM

Evaluation Results

Method	Links
NTI-SLSTM-LSTM2016.07		53.1
DMN2016.07		52.1
DiSAN2017.09		51.72
CNN-Tensor2017.09		51.2
NCSL2017.09		51.1
CT-LSTM2016.07		51
Tree-LSTM2017.09		51
LR-Bi-LSTM2017.09		50.6
NTI-SLSTM2016.07		50.5
Bi-LSTM with s2t self-attention2017.09		49.95
NLM+TSED+PT+2L2019.05		49.9
CNN-4002019.05		49.86
LM+TSED+PT+2L2019.05		49.82
DRNN2016.07		49.8
Bi-LSTM2017.09		49.8
DiSAN without directions2017.09		49.41
LM+TSED+PT+2L2019.05		49.37
LSTM-4002019.05		49.23
STM+TSED+PT+2L2019.05		49.19
Multi-head with s2t self-attention2017.09		49.14
STM+TSED+PT+2L2019.05		49.14
Bi-LSTM2016.07		49.1
STM+TSED2019.05		49.1
Word Embedding with s2t self-attention2017.09		48.87
NLM+TSED+PT+2L2019.05		48.87
NLM+TSED2019.05		48.81
LM+TSED2019.05		48.79
STM+TSED2019.05		48.77
LM+TSED2019.05		48.71
NLM+TSED2019.05		48.66
CNN-non-static2017.09		48
Word Embedding with additive attention2017.09		47.47
CNN-MC2016.07		47.4
2-layer LSTM2016.07		46
RNTN2016.07		45.7
RNTN2017.09		45.7
CNN-502019.05		45.07
ENC2019.05		44.94
MV-RNN2017.09		44.4
LSTM-502019.05		43.76