Reducing Spurious Correlations for Relation Extraction by Feature Decomposition and Semantic Augmentation

Abstract

Deep neural models have become mainstream in relation extraction (RE), yielding state-of-the-art performance. However, most existing neural models are prone to spurious correlations between input features and prediction labels, making the models suffer from low robustness and generalization.In this paper, we propose a spurious correlation reduction method for RE via feature decomposition and semantic augmentation (denoted as FDSA). First, we decompose the original sentence representation into class-related features and context-related features. To obtain better context-related features, we devise a contrastive learning method to pull together the context-related features of the anchor sentence and its augmented sentences, and push away the context-related features of different anchor sentences. In addition, we propose gradient-based semantic augmentation on context-related features in order to improve the robustness of the RE model. Experiments on four datasets show that our model outperforms the strong competitors.