Reducing Spurious Correlations for Answer Selection by Feature Decorrelation and Language Debiasing

1 code implementation • COLING 2022 • Zeyi Zhong, Min Yang, Ruifeng Xu

In this paper, we propose a novel Spurious Correlation reduction method to improve the robustness of the neural ANswer selection models (SCAN) from the sample and feature perspectives by removing the feature dependencies and language biases in answer selection.

Answer Selection Contrastive Learning