Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective (2022.coling-1)
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| Challenge: | Existing methods to debiase samples with biased features obstructs the model in learning from non-biased parts of the samples. |
| Approach: | They propose to eliminate spurious correlations in a fine-grained manner from a feature space perspective by using Random Fourier Features and weighted re-sampling to decorrelate dependencies between features. |
| Outcome: | The proposed method eliminates spurious correlations in a fine-grained manner from a feature space perspective. |
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| Challenge: | 'spurious correlations' have been used in NLP to informally denote any undesirable feature-label correlations. |
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| Challenge: | In text classification tasks, models often rely on spurious correlations for predictions, incorrectly associating irrelevant features with the target labels. |
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Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models (2022.findings-naacl)
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| Challenge: | Existing work identifies task-specific shortcuts via human priors or error analyses, which requires extensive expertise and efforts. |
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| Challenge: | Often these systems exceed human performance, but there is a caveat: standard benchmarks often assume that training and evaluation data are drawn independently and identically from the same underlying distribution. |
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