Feature-Level Debiased Natural Language Understanding

TL;DR

This paper introduces DCT, a contrastive learning-based debiasing method for NLU models that reduces reliance on dataset biases, improving out-of-distribution performance and representation fairness.

Contribution

The paper proposes a novel contrastive learning approach with dynamic sampling strategies to effectively mitigate biases in NLU models, addressing limitations of previous methods.

Findings

DCT outperforms state-of-the-art baselines on out-of-distribution datasets.

DCT reduces biased latent features in model representations.

DCT maintains in-distribution performance.

Abstract

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.