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Chinese Word Segmentation On Ctb6
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Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||
|---|---|---|
| LATTE (Linguistic units, lattices, PTMs, GNNs) | 98.07 | LATTE: Lattice ATTentive Encoding for Character-based Word Segmentation |
| BABERT-LE | 97.56 | Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling |
| BABERT | 97.45 | Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling |
| WMSeg + ZEN | 97.25 | Improving Chinese Word Segmentation with Wordhood Memory Networks |
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