CCG Supertagging on CCGbank

Metric: Accuracy (higher is better)

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	Heterogeneous Dynamic Convolutions	96.29	No	Geometry-Aware Supertagging with Heterogeneous D...	2022-03-23	Code
2	NeST-CCG + BERT	96.25	No	Supertagging Combinatory Categorial Grammar with...	2020-10-13	Code
3	CVT + Multi-task + Large	96.1	No	Semi-Supervised Sequence Modeling with Cross-Vie...	2018-09-22	Code
4	Lewis et al.	94.7	No	-	-	-
5	BiLSTM-LAN	94.7	No	Hierarchically-Refined Label Attention Network f...	2019-08-23	Code
6	Vaswani et al.	94.24	No	-	-	-
7	Low supervision	93.26	No	-	-	-
8	Xu et al.	93	No	-	-	-

#1Heterogeneous Dynamic ConvolutionsSOTA
96.29
Accuracy· 2022-03-23
Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions Code
#2NeST-CCG + BERTSOTA
96.25
Accuracy· 2020-10-13
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks Code
#3CVT + Multi-task + LargeSOTA
96.1
Accuracy· 2018-09-22
Semi-Supervised Sequence Modeling with Cross-View Training Code
#4Lewis et al.
94.7
Accuracy
No paper
#5BiLSTM-LAN
94.7
Accuracy· 2019-08-23
Hierarchically-Refined Label Attention Network for Sequence Labeling Code
#6Vaswani et al.
94.24
Accuracy
No paper
#7Low supervision
93.26
Accuracy
No paper
#8Xu et al.
93
Accuracy
No paper