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    <abstract>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 benchmarks show that READ significantly improves the OOD performance of BERT-based models, 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.</abstract>
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%0 Conference Proceedings
%T Robust Natural Language Understanding with Residual Attention Debiasing
%A Wang, Fei
%A Huang, James Y.
%A Yan, Tianyi
%A Zhou, Wenxuan
%A Chen, Muhao
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wang-etal-2023-robust
%X 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 benchmarks show that READ significantly improves the OOD performance of BERT-based models, 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.
%R 10.18653/v1/2023.findings-acl.32
%U https://aclanthology.org/2023.findings-acl.32
%U https://doi.org/10.18653/v1/2023.findings-acl.32
%P 504-519

Markdown (Informal)

[Robust Natural Language Understanding with Residual Attention Debiasing](https://aclanthology.org/2023.findings-acl.32) (Wang et al., Findings 2023)

• Robust Natural Language Understanding with Residual Attention Debiasing (Wang et al., Findings 2023)

ACL

• Fei Wang, James Y. Huang, Tianyi Yan, Wenxuan Zhou, and Muhao Chen. 2023. Robust Natural Language Understanding with Residual Attention Debiasing. In Findings of the Association for Computational Linguistics: ACL 2023, pages 504–519, Toronto, Canada. Association for Computational Linguistics.