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依存句法分析
Dependency Parsing On Penn Treebank
Dependency Parsing On Penn Treebank
评估指标
LAS
POS
UAS
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
LAS
POS
UAS
Paper Title
Repository
Label Attention Layer + HPSG + XLNet
96.26
97.3
97.42
Rethinking Self-Attention: Towards Interpretability in Neural Parsing
DMPar + XLNet
95.92
-
97.30
Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing
-
ACE
95.8
-
97.2
Automated Concatenation of Embeddings for Structured Prediction
Deep Biaffine + RoBERTa
95.75
-
97.29
Deep Biaffine Attention for Neural Dependency Parsing
HPSG Parser (Joint) + XLNet
95.72
97.3
97.20
Head-Driven Phrase Structure Grammar Parsing on Penn Treebank
MFVI
95.34
-
96.91
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
CVT + Multi-Task
95.02
-
96.61
Semi-Supervised Sequence Modeling with Cross-View Training
RNG Transformer
95.01
-
96.66
Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
SpanRel
94.70
-
96.44
Generalizing Natural Language Analysis through Span-relation Representations
CRFPar
94.49
-
96.14
Efficient Second-Order TreeCRF for Neural Dependency Parsing
Left-to-Right Pointer Network
94.43
-
96.04
Left-to-Right Dependency Parsing with Pointer Networks
Graph-based parser with GNNs
94.31
97.3
95.97
Graph-based Dependency Parsing with Graph Neural Networks
-
Deep Biaffine
94.22
-
95.87
Deep Biaffine Attention for Neural Dependency Parsing
Stack-Pointer Network
94.19
97.3
95.87
Stack-Pointer Networks for Dependency Parsing
jPTDP
93.87
97.97
95.51
An improved neural network model for joint POS tagging and dependency parsing
Experiment-bert
93.2
-
95.42
-
-
Andor et al.
92.79
97.44
94.61
Globally Normalized Transition-Based Neural Networks
Distilled neural FOG
92.06
97.44
94.26
Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser
Weiss et al.
92.06
97.3
94.01
Structured Training for Neural Network Transition-Based Parsing
-
BIST transition-based parser
91.9
97.44
93.99
Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
0 of 22 row(s) selected.
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