PLOG: Table-to-Logic Pretraining for Logical Table-to-Text Generation (2022.emnlp-main)
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| Challenge: | Logical table-to-text generation requires models to derive logical-level facts from table records via logical inference. |
| Approach: | They propose a pretrained logical form generator framework to improve generation fidelity . they use a dataset to test the logical inference accuracy of the framework . |
| Outcome: | The proposed framework outperforms baselines on LOGICNLG and CONTLOG on two benchmarks. |
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