Data To Text Generation On Mlb Dataset
Metrics
Precision
count
Results
Performance results of various models on this benchmark
Model Name | Precision | count | Paper Title | Repository |
|---|---|---|---|---|
| ENT | 81.1 | 23.8 | Data-to-text Generation with Macro Planning | |
| Macro | 94.4 | 30.8 | Data-to-text Generation with Macro Planning | |
| Force-Copy | 84.50 | 21.05 | May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation | - |
| SeqPlan | 95.9 | 28.9 | Data-to-text Generation with Variational Sequential Planning |
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