Robust Natural Language Understanding with Residual Attention Debiasing
Fei Wang James Y. Huang Tianyi Yan Wenxuan Zhou Muhao Chen
Natural language understanding (NLU) models often suffer from unintended
dataset biases. Among bias mitigation methods, ensemble-based debiasing
methods, especially product-of-experts (PoE), have stood out for their
impressive empirical success. However, previous ensemble-based debiasing
methods typically apply debiasing on top-level logits without directly
addressing biased attention patterns. Attention serves as the main media of
feature interaction and aggregation in PLMs and plays a crucial role in
providing robust prediction. In this paper, we propose REsidual Attention
Debiasing (READ), an end-to-end debiasing method that mitigates unintended
biases from attention. Experiments on three NLU tasks show that READ
significantly improves the performance of BERT-based models on OOD data with
shortcuts removed, including +12.9% accuracy on HANS, +11.0% accuracy on
FEVER-Symmetric, and +2.7% F1 on PAWS. Detailed analyses demonstrate the
crucial role of unbiased attention in robust NLU models and that READ
effectively mitigates biases in attention. Code is available at
https://github.com/luka-group/READ.