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Models/MFVI

MFVI

Reported on 10 benchmarks across 2 tasks · 2 papers · 8 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Natural Language Processing10 results

Dependency ParsingonChinese Treebank
LAS· 2020-10-10
91.69
SOTA
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training arXiv:2010.05003
Dependency ParsingonChinese Treebank
UAS· 2020-10-10
92.78
SOTA
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training arXiv:2010.05003
Semantic ParsingonDM
In-domain· 2019-06-19
94
best: 95.6 (ACE)
SOTA
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks arXiv:1906.07880
Semantic ParsingonDM
Out-of-domain· 2019-06-19
89.7
best: 92.6 (ACE)
SOTA
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks arXiv:1906.07880
Semantic ParsingonPSD
In-domain· 2019-06-19
81.4
best: 83.8 (ACE)
SOTA
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks arXiv:1906.07880
Semantic ParsingonPSD
Out-of-domain· 2019-06-19
79.6
best: 83.4 (ACE)
SOTA
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks arXiv:1906.07880
Semantic ParsingonPAS
In-domain· 2019-06-19
94.1
best: 95.8 (ACE)
SOTA
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks arXiv:1906.07880
Semantic ParsingonPAS
Out-of-domain· 2019-06-19
91.3
best: 94.6 (ACE)
SOTA
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks arXiv:1906.07880
Dependency ParsingonPenn Treebank
LAS· 2020-10-10
95.34
best: 96.26 (Label Attention Layer + HPSG + XLNet)
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training arXiv:2010.05003
Dependency ParsingonPenn Treebank
UAS· 2020-10-10
96.91
best: 97.42 (Label Attention Layer + HPSG + XLNet)
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training arXiv:2010.05003