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Constituency Parsing On Penn Treebank

Metrics

F1 score

Results

Performance results of various models on this benchmark

Paper Title
Hashing + XLNet96.43To be Continuous, or to be Discrete, Those are Bits of Questions
SAPar + XLNet96.40Improving Constituency Parsing with Span Attention
Label Attention Layer + HPSG + XLNet96.38Rethinking Self-Attention: Towards Interpretability in Neural Parsing
Attach-Juxtapose Parser + XLNet96.34Strongly Incremental Constituency Parsing with Graph Neural Networks
Head-Driven Phrase Structure Grammar Parsing (Joint) + XLNet96.33Head-Driven Phrase Structure Grammar Parsing on Penn Treebank
CRF Parser + RoBERTa96.32Fast and Accurate Neural CRF Constituency Parsing
Hashing + Bert96.03To be Continuous, or to be Discrete, Those are Bits of Questions
NFC + BERT-large95.92Investigating Non-local Features for Neural Constituency Parsing
N-ary semi-markov + BERT-large95.92N-ary Constituent Tree Parsing with Recursive Semi-Markov Model
Head-Driven Phrase Structure Grammar Parsing (Joint) + BERT95.84Head-Driven Phrase Structure Grammar Parsing on Penn Treebank
CRF Parser + BERT95.69Fast and Accurate Neural CRF Constituency Parsing
CNN Large + fine-tune95.6Cloze-driven Pretraining of Self-attention Networks
SpanRel95.5Generalizing Natural Language Analysis through Span-relation Representations
Tetra Tagging95.44Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Self-attentive encoder + ELMo95.13Constituency Parsing with a Self-Attentive Encoder
Model combination94.66Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
LSTM Encoder-Decoder + LSTM-LM94.47Direct Output Connection for a High-Rank Language Model
LSTM Encoder-Decoder + LSTM-LM94.32An Empirical Study of Building a Strong Baseline for Constituency Parsing
In-order94.2In-Order Transition-based Constituent Parsing
CRF Parser94.12Fast and Accurate Neural CRF Constituency Parsing
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