IBADR: an Iterative Bias-Aware Dataset Refinement Framework for
  Debiasing NLU models
Xiaoyue Wang Xin Liu Lijie Wang Yaoxiang Wang Jinsong Su Hua Wu
As commonly-used methods for debiasing natural language understanding (NLU)
models, dataset refinement approaches heavily rely on manual data analysis, and
thus maybe unable to cover all the potential biased features. In this paper, we
propose IBADR, an Iterative Bias-Aware Dataset Refinement framework, which
debiases NLU models without predefining biased features. We maintain an
iteratively expanded sample pool. Specifically, at each iteration, we first
train 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 use
these extended samples to train a sample generator. In this way, this generator
can effectively learn the correspondence relationship between bias indicators
and samples. Furthermore, we employ the generator to produce pseudo samples
with fewer biased features by feeding specific bias indicators. Finally, we
incorporate the generated pseudo samples into the pool. Experimental results
and in-depth analyses on two NLU tasks show that IBADR not only significantly
outperforms existing dataset refinement approaches, achieving SOTA, but also is
compatible with model-centric methods.