Papers by Aron Culotta

2 papers
Identifying Spurious Correlations for Robust Text Classification (2020.findings-emnlp)

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Challenge: Text classifiers often rely on spurious correlations to predict positive reviews . term Spielberg does not cause the review to be positive, so it does not affect the classification accuracy.
Approach: They propose a method to distinguish spurious and genuine correlations in text classification using treatment effect estimators.
Outcome: The proposed method works well even with limited training examples and is possible to transport the word classifier to new domains.
Using Text-Based Causal Inference to Disentangle Factors Influencing Online Review Ratings (2025.naacl-long)

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Challenge: Existing methods to analyze online reviews for aspects of quality are limited . authors propose a method to disentangle the impact of each aspect on overall perception .
Approach: They propose a method to disentangle the effect of each aspect on overall perception . they use textual mentions in reviews as proxies for real-world attributes .
Outcome: The proposed method improves on real-world reviews of U.S. K-12 schools.

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