Constituency Parsing On Penn Treebank

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		Paper Title
Hashing + XLNet	96.43	To be Continuous, or to be Discrete, Those are Bits of Questions
SAPar + XLNet	96.40	Improving Constituency Parsing with Span Attention
Label Attention Layer + HPSG + XLNet	96.38	Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Attach-Juxtapose Parser + XLNet	96.34	Strongly Incremental Constituency Parsing with Graph Neural Networks
Head-Driven Phrase Structure Grammar Parsing (Joint) + XLNet	96.33	Head-Driven Phrase Structure Grammar Parsing on Penn Treebank
CRF Parser + RoBERTa	96.32	Fast and Accurate Neural CRF Constituency Parsing
Hashing + Bert	96.03	To be Continuous, or to be Discrete, Those are Bits of Questions
NFC + BERT-large	95.92	Investigating Non-local Features for Neural Constituency Parsing
N-ary semi-markov + BERT-large	95.92	N-ary Constituent Tree Parsing with Recursive Semi-Markov Model
Head-Driven Phrase Structure Grammar Parsing (Joint) + BERT	95.84	Head-Driven Phrase Structure Grammar Parsing on Penn Treebank
CRF Parser + BERT	95.69	Fast and Accurate Neural CRF Constituency Parsing
CNN Large + fine-tune	95.6	Cloze-driven Pretraining of Self-attention Networks
SpanRel	95.5	Generalizing Natural Language Analysis through Span-relation Representations
Tetra Tagging	95.44	Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Self-attentive encoder + ELMo	95.13	Constituency Parsing with a Self-Attentive Encoder
Model combination	94.66	Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
LSTM Encoder-Decoder + LSTM-LM	94.47	Direct Output Connection for a High-Rank Language Model
LSTM Encoder-Decoder + LSTM-LM	94.32	An Empirical Study of Building a Strong Baseline for Constituency Parsing
In-order	94.2	In-Order Transition-based Constituent Parsing
CRF Parser	94.12	Fast and Accurate Neural CRF Constituency Parsing

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Constituency Parsing On Penn Treebank

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