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Data To Text Generation On Webnlg

Metrics

BLEU

Results

Performance results of various models on this benchmark

Paper TitleCode
Control Prefixes (A1, T5-large)67.32Control Prefixes for Parameter-Efficient Text Generation
Control Prefixes (A1, A2, T5-large)67.15Control Prefixes for Parameter-Efficient Text Generation
JointGT Baseline67.08FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation
T5-B Baseline67.04FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation
T5-large + Wiki + Position66.07Stage-wise Fine-tuning for Graph-to-Text Generation
HTML (fine-tuning)65.4HTLM: Hyper-Text Pre-Training and Prompting of Language Models
T5-small65.05Investigating Pretrained Language Models for Graph-to-Text Generation
TrICy (trK = trk* = 0.24)64.73TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
T5-Base64.7Text-to-Text Pre-Training for Data-to-Text Tasks
TrICy (trK = 0)64.08TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy
CGE-LW (Levi Graph)63.69Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
Multiview-G2S62.89Structural Information Preserving for Graph-to-Text Generation
Graformer61.15Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
GTR-LSTM (entity masking)58.6GTR-LSTM: A Triple Encoder for Sentence Generation from RDF Data
E2E GRU57.20Neural data-to-text generation: A comparison between pipeline and end-to-end architectures
GCN EC55.9Deep Graph Convolutional Encoders for Structured Data to Text Generation
BestPlan47.4Step-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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