Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis

Anonymous

Abstract:

Recent research has revealed that deep learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances. For instance, a sentiment classifier may erroneously learn that the token "performances" is commonly associated with positive movie reviews.
Relying on these spurious correlations degrades the classifier’s performance when it deploys on out-of-distribution data.
In this paper, we examine the implications of spurious correlations through a novel perspective called neighborhood analysis. The analysis uncovers how spurious correlations lead unrelated words to erroneously cluster together in the embedding space. Driven by the analysis, we design a metric to detect spurious tokens and also propose a family of regularization methods, NFL (doN't Forget your Language) to mitigate spurious correlations in text classification.
Experiments show that NFL can effectively prevent erroneous clusters and significantly improve the robustness of classifiers.

Paper Type: long

Research Area: Interpretability and Analysis of Models for NLP

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

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