CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification

Akbar Karimi, Lucie Flek
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引用次数: 1

Abstract

Class imbalance problem can cause machine learning models to produce an undesirable performance on the minority class as well as the whole dataset. Using data augmentation techniques to increase the number of samples is one way to tackle this problem. We introduce a novel counterfactual data augmentation by verb replacement for the identification of medical claims. In addition, we investigate the impact of this method and compare it with 3 other data augmentation techniques, showing that the proposed method can result in significant (relative) improvement on the minority class.
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任务8:利用反事实数据增强减轻因果索赔识别中的类不平衡
类不平衡问题会导致机器学习模型在少数类和整个数据集上产生不理想的性能。使用数据增强技术来增加样本数量是解决这个问题的一种方法。我们引入了一种新的反事实数据增强动词替换医疗索赔的识别。此外,我们还研究了该方法的影响,并将其与其他3种数据增强技术进行了比较,结果表明,所提出的方法可以显著(相对)改善少数类别。
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