Ccg Supertagging On Ccgbank
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| Xu et al. | 93.00 | - | - |
| BiLSTM-LAN | 94.7 | Hierarchically-Refined Label Attention Network for Sequence Labeling | |
| Lewis et al. | 94.7 | - | - |
| CVT + Multi-task + Large | 96.1 | Semi-Supervised Sequence Modeling with Cross-View Training | |
| Heterogeneous Dynamic Convolutions | 96.29 | Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions | |
| NeST-CCG + BERT | 96.25 | Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks | |
| Low supervision | 93.26 | - | - |
| Vaswani et al. | 94.24 | - | - |
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