Papers › Towards Debiasing NLU Models from Unknown Biases

Towards Debiasing NLU Models from Unknown Biases

25 Sep 2020EMNLP 2020 11arXiv:2009.12303archive 2025-07-28

Prasetya Ajie Utama, Nafise Sadat Moosavi, Iryna Gurevych

NLU models often exploit biases to achieve high dataset-specific performance without properly learning the intended task. Recently proposed debiasing methods are shown to be effective in mitigating this tendency. However, these methods rely on a major assumption that the types of bias should be known a-priori, which limits their application to many NLU tasks and datasets. In this work, we present the first step to bridge this gap by introducing a self-debiasing framework that prevents models from mainly utilizing biases without knowing them in advance. The proposed framework is general and complementary to the existing debiasing methods. We show that it allows these existing methods to retain the improvement on the challenge datasets (i.e., sets of examples designed to expose models' reliance on biases) without specifically targeting certain biases. Furthermore, the evaluation suggests that applying the framework results in improved overall robustness.

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