Constituency Parsing On Penn Treebank

评估指标

F1 score

评测结果

各个模型在此基准测试上的表现结果

模型名称	F1 score	Paper Title	Repository
Model combination	94.66	Improving Neural Parsing by Disentangling Model Combination and Reranking Effects	-
Label Attention Layer + HPSG + XLNet	96.38	Rethinking Self-Attention: Towards Interpretability in Neural Parsing
RNN Grammar	93.3	Recurrent Neural Network Grammars
Tetra Tagging	95.44	Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Head-Driven Phrase Structure Grammar Parsing (Joint) + XLNet	96.33	Head-Driven Phrase Structure Grammar Parsing on Penn Treebank
Hashing + XLNet	96.43	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
Stack-only RNNG	93.6	What Do Recurrent Neural Network Grammars Learn About Syntax?
CRF Parser + RoBERTa	96.32	Fast and Accurate Neural CRF Constituency Parsing
Transformer	92.7	Attention Is All You Need
Self-attentive encoder + ELMo	95.13	Constituency Parsing with a Self-Attentive Encoder
SAPar + XLNet	96.40	Improving Constituency Parsing with Span Attention
Semi-supervised LSTM-LM	93.8	-	-
CNN Large + fine-tune	95.6	Cloze-driven Pretraining of Self-attention Networks	-
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
Parse fusion	92.6	-	-
LSTM Encoder-Decoder + LSTM-LM	94.47	Direct Output Connection for a High-Rank Language Model
Attach-Juxtapose Parser + XLNet	96.34	Strongly Incremental Constituency Parsing with Graph Neural Networks
Self-training	92.1	Effective Self-Training for Parsing

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