Grammatical Error Detection On Conll 2014 A2
Metriken
F0.5
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | F0.5 | Paper Title | Repository |
|---|---|---|---|
| BiLSTM-JOINT (trained on FCE) | 29.65 | Jointly Learning to Label Sentences and Tokens | |
| Bi-LSTM + POS (trained on FCE) | 26.2 | Auxiliary Objectives for Neural Error Detection Models | - |
| Bi-LSTM (trained on FCE) | 23.9 | Compositional Sequence Labeling Models for Error Detection in Learner Writing | - |
| Bi-LSTM + POS (unrestricted data) | 45.1 | Auxiliary Objectives for Neural Error Detection Models | - |
| VERNet | 63.1 | Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction | |
| Bi-LSTM + LMcost (trained on FCE) | 25.88 | Semi-supervised Multitask Learning for Sequence Labeling | |
| Ann+PAT+MT | 30.13 | Artificial Error Generation with Machine Translation and Syntactic Patterns | - |
| Bi-LSTM (unrestricted data) | 44.0 | Compositional Sequence Labeling Models for Error Detection in Learner Writing | - |
0 of 8 row(s) selected.