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Change Logs:
- 2023-09-26: First draft. The paper appears at EMNLP 2021.
- The following is the main claim of the paper, as is summarized in [1]:
[…] all correlations between labels and individual “input features” are spurious.
- Spurious correlation is useful in the training data but unreliable in general [1].
Reference
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Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language (Eisenstein, NAACL 2022): This paper updates the claim in the main paper theoretically: feature-label correlation is not related to whether label is invariant to the the interventions on the feature.
Practically, the paper suggests the partial invariance (whether independent or not) for real-world datasets; for example, the sentiment of a movie review is invariant to the actor names. The paper also suggest the following options to improve model robustness:
data augmentation, causally-motivated regularizers, stress tests, and “worst-subgroup” performance metrics (and associated robust optimizers) can be seen as enforcing or testing task-specific invariance properties that provide robustness against known distributional shifts (e.g., Lu et al., 2020; Ribeiro et al., 2020; Kaushik et al., 2021; Koh et al., 2021; Veitch et al., 2021). Such approaches generally require domain knowledge about the linguistic and causal properties of the task at hand — or to put it more positively, they make it possible for such domain knowledge to be brought to bear. Indeed, the central argument of this paper is that no meaningful definition of spuriousness or robustness can be obtained without such domain knowledge.
- [1807.11714] Gender Bias in Neural Natural Language Processing (Lu et al.)
- [2010.02114] Explaining The Efficacy of Counterfactually Augmented Data (Kaushik et al., ICLR 2021)
- [2106.00545] Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests (Veitch et al., NeurIPS 2021)
- Koh et al. and Ribeiro et al. correspond to the WILDS paper and the CheckList paper.
- On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations (Schwartz & Stanovsky, Findings 2022): This paper shows that creating a truly balanced dataset devoid of the issues mentioned in the main paper will also throw the useful signals encoded in the texts (“throw the baby out with the bathwater”).