Papers with Spurious Correlation reduction method
Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing (2022.coling-1)
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| Challenge: | Existing deep neural models rely on spurious correlations between prediction labels and input features, which in general suffer from robustness and generalization. |
| Approach: | They propose a feature decorrelation module to remove feature dependencies and reduce spurious correlations by learning a weight for each instance at the training phase. |
| Outcome: | The proposed method improves the robustness of the neural ANswer selection models from the sample and feature perspectives. |