Grammatical Error Correction On Bea 2019 Test
評価指標
F0.5
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
| モデル名 | F0.5 |
|---|---|
| frustratingly-easy-system-combination-for | 79.90 |
| efficient-and-interpretable-grammatical-error | 74.07 |
| neural-quality-estimation-with-multiple | 68.9 |
| pillars-of-grammatical-error-correction | 81.4 |
| neural-grammatical-error-correction-systems | 69.5 |
| unsupervised-grammatical-error-correction | 76.5 |
| redpennet-for-grammatical-error-correction | 77.60 |
| gector-grammatical-error-correction-tag-not | 73.7 |
| an-empirical-study-of-incorporating-pseudo | 70.2 |
| improving-seq2seq-grammatical-error | 73.1 |
| encoder-decoder-models-can-benefit-from-pre | 69.8 |
| lm-critic-language-models-for-unsupervised | 72.9 |
| learning-to-combine-grammatical-error | 73.2 |
| improved-grammatical-error-correction-by | 77.1 |
| system-combination-via-quality-estimation-for | 80.84 |
| ensembling-and-knowledge-distilling-of-large-1 | 76.05 |
| the-laix-systems-in-the-bea-2019-gec-shared | 66.78 |
| gector-grammatical-error-correction-tag-not | 72.4 |
| a-neural-grammatical-error-correction-system | 69.0 |