Data To Text Generation On Rotowire Relation
评估指标
Precision
count
评测结果
各个模型在此基准测试上的表现结果
模型名称 | Precision | count | Paper Title | Repository |
|---|---|---|---|---|
| SeqPlan | 97.6 | 46.7 | Data-to-text Generation with Variational Sequential Planning | |
| Force-Copy | 95.40% | 27.37 | May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation | - |
| Macro | 97.6 | 42.1 | Data-to-text Generation with Macro Planning | |
| Neural Content Planning + conditional copy | 87.47% | 34.28 | Data-to-Text Generation with Content Selection and Planning | |
| Encoder-decoder + conditional copy | 74.80% | 23.72 | Challenges in Data-to-Document Generation | |
| Hierarchical Transformer Encoder + conditional copy | 89.46% | 21.17 | A Hierarchical Model for Data-to-Text Generation |
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