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SOTA
数据到文本生成
Data To Text Generation On Webnlg
Data To Text Generation On Webnlg
评估指标
BLEU
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
BLEU
Paper Title
Repository
Control Prefixes (A1, T5-large)
67.32
Control Prefixes for Parameter-Efficient Text Generation
Control Prefixes (A1, A2, T5-large)
67.15
Control Prefixes for Parameter-Efficient Text Generation
JointGT Baseline
67.08
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation
-
T5-B Baseline
67.04
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation
-
T5-large + Wiki + Position
66.07
Stage-wise Fine-tuning for Graph-to-Text Generation
HTML (fine-tuning)
65.4
HTLM: Hyper-Text Pre-Training and Prompting of Language Models
-
T5-small
65.05
Investigating Pretrained Language Models for Graph-to-Text Generation
TrICy (trK = trk* = 0.24)
64.73
TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
-
T5-Base
64.7
Text-to-Text Pre-Training for Data-to-Text Tasks
TrICy (trK = 0)
64.08
TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
-
CGE-LW (Levi Graph)
63.69
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
Multiview-G2S
62.89
Structural Information Preserving for Graph-to-Text Generation
Graformer
61.15
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
-
GTR-LSTM (entity masking)
58.6
GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
-
E2E GRU
57.20
Neural data-to-text generation: A comparison between pipeline and end-to-end architectures
GCN EC
55.9
Deep Graph Convolutional Encoders for Structured Data to Text Generation
BestPlan
47.4
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
BART (TextBox 2.0)
-
TextBox 2.0: A Text Generation Library with Pre-trained Language Models
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Data To Text Generation On Webnlg | SOTA | HyperAI超神经