Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples (2022.emnlp-main)
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| Challenge: | State-of-the-art methods based on pre-trained models have achieved remarkable performance on the standard test dataset. |
| Approach: | They propose to incorporate hierarchical structure of logical forms into the model and exploit automatically generated counterfactual data for training. |
| Outcome: | The proposed method is effective to alleviate spurious correlations between the headers of the tables and operators of the logical form. |
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