Counterfactual Augmentation for Multimodal Learning Under Presentation Bias (2023.findings-emnlp)
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| Challenge: | In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage. |
| Approach: | They propose a method for correcting presentation bias using generated counterfactual labels by augmentation of the labels by the user. |
| Outcome: | The proposed method improves performance in an oracle setting compared to uncorrected models and existing bias-correction methods. |
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