Tense Reinflection on tense reinflection without auxiliary do (test)
100Full AccuracyTf
Evaluation Results
| Method | Links | |
|---|---|---|
| TfSource=Ahuja et al. (2025), Training objective=language modeling objective2025.11 | 100 | |
| TfSource=Qin et al. (2025)2025.11 | 100 | |
| TfArchitecture=Transformer, Ground-truth parse trees=true2025.11 | 99.9 | |
| Stack Attention TransformerArchitecture=Tf+Nd2025.11 | 99.8 | |
| Tf+Nd+NdArchitecture=Transformer with Double Stack2025.11 | 99.7 | |
| Tf+SupArchitecture=Transformer with Supervision2025.11 | 99.3 | |
| Tf+Sup+SupArchitecture=Transformer with Double Supervision2025.11 | 98.4 | |
| LSTMSource=McCoy et al. (2020)2025.11 | 96 | |
| Tree-GRUSource=McCoy et al. (2020)2025.11 | 96 | |
| ON-LSTMSource=McCoy et al. (2020)2025.11 | 95 | |
| Stack RNNArchitecture=RNN+Nd2025.11 | 5.9 | |
| RNNArchitecture=RNN2025.11 | 5.1 | |
| LSTMArchitecture=LSTM2025.11 | 2.9 | |
| RNN+SupArchitecture=RNN with Supervision2025.11 | 2.4 | |
| RNN+Nd+RArchitecture=Stack RNN with Reinforcement2025.11 | 2.4 | |
| LSTM+Sup+RArchitecture=LSTM with Supervision and Reinforcement2025.11 | 1.8 | |
| RNN+Sup+RArchitecture=RNN with Supervision and Reinforcement2025.11 | 1.7 | |
| LSTM+Nd+RArchitecture=Stack LSTM with Reinforcement2025.11 | 1.7 | |
| LSTM+SupArchitecture=LSTM with Supervision2025.11 | 1.6 | |
| Stack LSTMArchitecture=LSTM+Nd2025.11 | 1.2 |