OpenCodePapers

kg-to-text-generation-on-webnlg-2-0-1

KG-to-Text Generation
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PaperCodeBLEUMETEORROUGEFactSpotterModelNameReleaseDate
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation✓ Link67.0848.3499.71FactT5B2023-10-25
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation✓ Link67.0848.3499.44JointGT Baseline2023-10-25
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation✓ Link67.0448.3599.05T5B Baseline2023-10-25
FactSpotter: Evaluating the Factual Faithfulness of Graph-to-Text Generation✓ Link66.8948.1999.67FactJointGT2023-10-25
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs✓ Link61.0146.3273.57JointGT (T5)2021-06-19
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs✓ Link58.6646.0473.06T52021-06-19
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs✓ Link58.5545.0172.31JointGT (BART)2021-06-19
JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs✓ Link56.6544.5170.94BART2021-06-19
Handling Rare Items in Data-to-Text Generation✓ Link48.036.065.0SOTA-NPT 2018-11-01
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