Grammatical Error Correction On Jfleg
Métriques
GLEU
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | GLEU | Paper Title | Repository |
|---|---|---|---|
| Transformer + Pre-train with Pseudo Data + BERT | 62.0 | Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction | - |
| Copy-augmented Model (4 Ensemble +Denoising Autoencoder) | 61.0 | Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data | - |
| Transformer | 59.9 | Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task | - |
| VERNet | 62.1 | Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction | - |
| SMT + BiGRU | 61.5 | Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation | - |
| CNN Seq2Seq | 57.47 | A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction | - |
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