Question Formation on English Question Formation (test)
100Full AccuracyTf
Evaluation Results
| Method | Links | |
|---|---|---|
| TfSource=Ahuja et al. (2025), Training objective=language modeling objective2025.11 | 100 | |
| TfSource=Qin et al. (2025), Uses ground-truth parse trees=true2025.11 | 100 | |
| TfArchitecture=Transformer2025.11 | 99.9 | |
| Tf+SupArchitecture=Transformer + Supervision2025.11 | 99.9 | |
| Tf+NdArchitecture=Stack Attention Transformer2025.11 | 99.9 | |
| Tf+Sup+SupArchitecture=Transformer + Supervision + Supervision2025.11 | 99.8 | |
| Tf+Nd+NdArchitecture=Multi-Stack Attention Transformer2025.11 | 99.5 | |
| Tree-GRUArchitecture=Tree-structured GRU, Source=McCoy et al. (2020)2025.11 | 96 | |
| ON-LSTMArchitecture=Ordered Neurons LSTM, Source=McCoy et al. (2020)2025.11 | 93 | |
| GRU+loc. attn.Architecture=GRU + local attention, Source=McCoy et al. (2020)2025.11 | 77 | |
| RNNArchitecture=RNN2025.11 | 46.5 | |
| LSTM+Nd+RArchitecture=Stack LSTM + Recurrent2025.11 | 11.8 | |
| RNN+NdArchitecture=Stack RNN2025.11 | 2.7 | |
| RNN+SupArchitecture=RNN + Supervision2025.11 | 2.2 | |
| RNN+Nd+RArchitecture=Stack RNN + Recurrent2025.11 | 1.9 | |
| RNN+Sup+RArchitecture=RNN + Supervision + Recurrent2025.11 | 1.1 | |
| LSTM+Sup+RArchitecture=LSTM + Supervision + Recurrent2025.11 | 0.5 | |
| LSTMArchitecture=LSTM2025.11 | 0.3 | |
| LSTM+SupArchitecture=LSTM + Supervision2025.11 | 0.2 | |
| LSTM+NdArchitecture=Stack LSTM2025.11 | 0.2 |