Sentiment Classification on SST2 phrase
93.96AccuracyBERT+SCM
Evaluation Results
| Method | Links | |
|---|---|---|
| BERT+SCMModel type=Latent semantic tree models, Backbone=BERT2023.08 | 93.96 | |
| BERT (2019)Model type=Sequential models2023.08 | 93.52 | |
| Kim et al., 2019Model type=Untagged tree (by external parser) models2023.08 | 91.3 | |
| Gumbel-Tree (2018)Model type=Latent untagged tree models2023.08 | 90.7 | |
| BiTreeLSTM (2017)Model type=Sentiment tree models2023.08 | 90.3 | |
| RTCM (2019)Model type=Sentiment tree models2023.08 | 90.3 | |
| Havrylov et al., 2019Model type=Latent untagged tree models2023.08 | 90.2 | |
| BILSTM+SCMModel type=Latent semantic tree models, Backbone=BiLSTM2023.08 | 90.06 | |
| TreeLSTM+LVG (2019)Model type=Sentiment tree models2023.08 | 89.8 | |
| TreeLSTM+LVEG (2019)Model type=Sentiment tree models2023.08 | 89.8 | |
| TreeLSTM+WG (2019)Model type=Sentiment tree models2023.08 | 89.7 | |
| BILSTM (1997)Model type=Sequential models2023.08 | 89.68 | |
| CRVNN (2021)Model type=Latent untagged tree models2023.08 | 88.3 | |
| RL-SPINN (2017)Model type=Latent untagged tree models2023.08 | 86.5 | |
| RNTN (2013)Model type=Sentiment tree models2023.08 | 85.4 | |
| MVRNN (2013)Model type=Sentiment tree models2023.08 | 82.9 |