Grammatical Error Detection On Conll 2014 A1
Métriques
F0.5
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | F0.5 | Paper Title | Repository |
|---|---|---|---|
| BiLSTM-JOINT (trained on FCE) | 22.14 | Jointly Learning to Label Sentences and Tokens | |
| Bi-LSTM + POS (unrestricted data) | 36.1 | Auxiliary Objectives for Neural Error Detection Models | - |
| Ann+PAT+MT | 21.87 | Artificial Error Generation with Machine Translation and Syntactic Patterns | - |
| Bi-LSTM + LMcost (trained on FCE) | 17.86 | Semi-supervised Multitask Learning for Sequence Labeling | |
| Bi-LSTM (unrestricted data) | 34.3 | Compositional Sequence Labeling Models for Error Detection in Learner Writing | - |
| Bi-LSTM (trained on FCE) | 16.4 | Compositional Sequence Labeling Models for Error Detection in Learner Writing | - |
| Bi-LSTM + POS (trained on FCE) | 17.5 | Auxiliary Objectives for Neural Error Detection Models | - |
| VERNet | 54.3 | Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction |
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