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SotA/Natural Language Processing/Dependency Parsing/Penn Treebank

Dependency Parsing on Penn Treebank

Metric: POS (higher is better)

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#Model↕POS▼Extra DataPaperDate↕Code
1jPTDP97.97NoAn improved neural network model for joint POS t...2018-07-11Code
2Andor et al.97.44NoGlobally Normalized Transition-Based Neural Netw...2016-03-19Code
3Distilled neural FOG97.44NoDistilling an Ensemble of Greedy Dependency Pars...2016-09-24Code
4BIST transition-based parser97.44NoSimple and Accurate Dependency Parsing Using Bid...2016-03-14Code
5Label Attention Layer + HPSG + XLNet97.3NoRethinking Self-Attention: Towards Interpretabil...2019-11-10Code
6HPSG Parser (Joint) + XLNet 97.3NoHead-Driven Phrase Structure Grammar Parsing on ...2019-07-05Code
7Graph-based parser with GNNs97.3No--Code
8Stack-Pointer Network97.3NoStack-Pointer Networks for Dependency Parsing2018-05-03Code
9Weiss et al.97.3NoStructured Training for Neural Network Transitio...2015-06-19-
10Arc-hybrid97.3NoTraining with Exploration Improves a Greedy Stac...2016-03-11-
11BIST graph-based parser97.3NoSimple and Accurate Dependency Parsing Using Bid...2016-03-14Code