Summary of Fighting Spurious Correlations in Text Classification Via a Causal Learning Perspective, by Yuqing Zhou et al.
Fighting Spurious Correlations in Text Classification via a Causal Learning Perspective
by Yuqing Zhou, Ziwei Zhu
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Causally Calibrated Robust Classifier (CCR) aims to improve text classification model robustness by reducing reliance on spurious correlations. This is achieved through a causal feature selection method based on counterfactual reasoning and an unbiased inverse propensity weighting (IPW) loss function. By selecting causal features, the model relies less on irrelevant features during prediction, enhancing its performance and generalization capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new text classification approach called Causally Calibrated Robust Classifier (CCR) helps models make better predictions by avoiding incorrect associations with target labels. This is important because models often use irrelevant information to guess what a piece of text says. The CCR method picks the most relevant features and ignores unimportant ones, making it more reliable and accurate. |
Keywords
» Artificial intelligence » Feature selection » Generalization » Loss function » Text classification
