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As commonly-used methods for debiasing natural language understanding (NLU)models, dataset refinement approaches heavily rely on manual data analysis, andthus maybe unable to cover all the potential biased features. In this paper, wepropose IBADR, an Iterative Bias-Aware Dataset Refinement framework, whichdebiases NLU models without predefining biased features. We maintain aniteratively expanded sample pool. Specifically, at each iteration, we firsttrain a shallow model to quantify the bias degree of samples in the pool. Then,we pair each sample with a bias indicator representing its bias degree, and usethese extended samples to train a sample generator. In this way, this generatorcan effectively learn the correspondence relationship between bias indicatorsand samples. Furthermore, we employ the generator to produce pseudo sampleswith fewer biased features by feeding specific bias indicators. Finally, weincorporate the generated pseudo samples into the pool. Experimental resultsand in-depth analyses on two NLU tasks show that IBADR not only significantlyoutperforms existing dataset refinement approaches, achieving SOTA, but also iscompatible with model-centric methods.

IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models