Automated Essay Scoring on ASAP Kaggle 2.0 (test)
0.84QWKModel 5 (Linguistic features)
Evaluation Results
| Method | Links | |
|---|---|---|
| Model 5 (Linguistic features)Input=Linguistic features, Model=Majority voting ensembles2026.01 | 0.84 | |
| HOTeam name=HO2026.01 | 0.833 | |
| yaoTeam name=yao2026.01 | 0.831 | |
| GPU From onethingai.comTeam name=GPU From onethingai.com2026.01 | 0.828 | |
| Model 5 (Linguistic features)Input=Linguistic features, Model=Majority voting ensembles2026.01 | 0.822 | |
| Model 1 (Concatenating SLMs embeddings)Input=Concatenating SLMs embeddings, Model=MLP2026.01 | 0.815 | |
| Model 3 (DeBERTa embeddings)Input=DeBERTa embeddings, Model=XGBoost/LightGBM2026.01 | 0.812 | |
| Model 1 (Concatenating SLMs embeddings)Input=Concatenating SLMs embeddings, Model=MLP2026.01 | 0.807 | |
| Model 2 (RoBERTa embeddings)Input=RoBERTa embeddings, Model=XGBoost/LightGBM2026.01 | 0.783 | |
| Model 4 (BERT embeddings)Input=BERT embeddings, Model=XGBoost/LightGBM2026.01 | 0.781 | |
| Model 3 (DeBERTa embeddings)Input=DeBERTa embeddings, Model=XGBoost/LightGBM2026.01 | 0.776 | |
| Model 2 (RoBERTa embeddings)Input=RoBERTa embeddings, Model=XGBoost/LightGBM2026.01 | 0.754 | |
| Model 4 (BERT embeddings)Input=BERT embeddings, Model=XGBoost/LightGBM2026.01 | 0.735 |