Sentiment Analysis On Multi Domain Sentiment
Métriques
Average
Books
DVD
Electronics
Kitchen
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Average | Books | DVD | Electronics | Kitchen | Paper Title | Repository |
|---|---|---|---|---|---|---|---|
| Asymmetric tri-training | 78.39 | 72.97 | 76.17 | 80.47 | 83.97 | Asymmetric Tri-training for Unsupervised Domain Adaptation | |
| Multi-task tri-training | 79.15 | 74.86 | 78.14 | 81.45 | 82.14 | Strong Baselines for Neural Semi-supervised Learning under Domain Shift | |
| Distributional Correspondence Indexing | 83.30 | 81.4 | 81.00 | 85,06 | 85.9 | Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments | |
| UDALM: Unsupervised Domain Adaptation through Language Modeling | 91.74 | 90.63 | 89.78 | 92.78 | 93.77 | UDALM: Unsupervised Domain Adaptation through Language Modeling | |
| DANN | 76.26 | 71.43 | 75.4 | 77.67 | 80.53 | Domain-Adversarial Training of Neural Networks | |
| VFAE | 78.36 | 73.40 | 76.57 | 80.53 | 82.93 | The Variational Fair Autoencoder |
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