Identifying Spurious Correlations for Robust Text Classification
This addresses robustness issues in text classification for applications like sentiment analysis and toxicity detection, but it is incremental as it builds on existing treatment effect estimators.
The paper tackled the problem of spurious correlations driving text classifier predictions by proposing a method to distinguish spurious from genuine correlations, resulting in improved worst-case accuracy on affected samples in sentiment classification and toxicity detection datasets.
The predictions of text classifiers are often driven by spurious correlations -- e.g., the term `Spielberg' correlates with positively reviewed movies, even though the term itself does not semantically convey a positive sentiment. In this paper, we propose a method to distinguish spurious and genuine correlations in text classification. We treat this as a supervised classification problem, using features derived from treatment effect estimators to distinguish spurious correlations from "genuine" ones. Due to the generic nature of these features and their small dimensionality, we find that the approach works well even with limited training examples, and that it is possible to transport the word classifier to new domains. Experiments on four datasets (sentiment classification and toxicity detection) suggest that using this approach to inform feature selection also leads to more robust classification, as measured by improved worst-case accuracy on the samples affected by spurious correlations.