  Natural language understanding (NLU) models often rely on dataset biases
rather than intended task-relevant features to achieve high performance on
specific datasets. As a result, these models perform poorly on datasets outside
the training distribution. Some recent studies address this issue by reducing
the weights of biased samples during the training process. However, these
methods still encode biased latent features in representations and neglect the
dynamic nature of bias, which hinders model prediction. We propose an NLU
debiasing method, named debiasing contrastive learning (DCT), to simultaneously
alleviate the above problems based on contrastive learning. We devise a
debiasing, positive sampling strategy to mitigate biased latent features by
selecting the least similar biased positive samples. We also propose a dynamic
negative sampling strategy to capture the dynamic influence of biases by
employing a bias-only model to dynamically select the most similar biased
negative samples. We conduct experiments on three NLU benchmark datasets.
Experimental results show that DCT outperforms state-of-the-art baselines on
out-of-distribution datasets while maintaining in-distribution performance. We
also verify that DCT can reduce biased latent features from the model's
representation.
