通过语义三元组重构文本，消除模型对虚假关联的依赖。
CoBA: Counterbias Text Augmentation for Mitigating Various Spurious Correlations via Semantic Triples
- 将文本拆解为主谓宾三元组，针对性修改以打破虚假关联。
- 在多个数据集上提升下游任务性能，降低性别、简单性等偏差。
- 适合需要提升模型鲁棒性和公平性的NLP应用场景。
深度学习模型常会学习并利用训练数据中的虚假相关性，依赖非目标特征进行预测，导致在未见数据上表现下降和泛化能力差。为此，我们提出一种更通用的反事实数据增强方法——反偏见数据增强（Counterbias Data Augmentation），可同时应对多种偏差（如性别偏见、简单性偏见），并增强分布外鲁棒性。本文提出CoBA：反偏见增强框架，其在语义三元组层面操作：先将文本分解为主-谓-宾三元组，再选择性地修改这些三元组以破坏虚假相关性，最后通过重构调整后的三元组生成反偏见数据。大量实验表明，CoBA不仅提升了下游任务性能，还有效降低了偏差，并增强了分布外适应能力，为应对虚假相关性挑战提供了一种通用且稳健的解决方案。
原文摘要 · Abstract (English)
Deep learning models often learn and exploit spurious correlations in training data, using these non-target features to inform their predictions. Such reliance leads to performance degradation and poor generalization on unseen data. To address these limitations, we introduce a more general form of counterfactual data augmentation, termed counterbias data augmentation, which simultaneously tackles multiple biases (e.g., gender bias, simplicity bias) and enhances out-of-distribution robustness. We present CoBA: CounterBias Augmentation, a unified framework that operates at the semantic triple level: first decomposing text into subject-predicate-object triples, then selectively modifying these triples to disrupt spurious correlations. By reconstructing the text from these adjusted triples, CoBA generates counterbias data that mitigates spurious patterns. Through extensive experiments, we demonstrate that CoBA not only improves downstream task performance, but also effectively reduces biases and strengthens out-of-distribution resilience, offering a versatile and robust solution to the challenges posed by spurious correlations.
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