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The class imbalance problem can cause machine learning models to produce anundesirable performance on the minority class as well as the whole dataset.Using data augmentation techniques to increase the number of samples is one wayto tackle this problem. We introduce a novel counterfactual data augmentationby verb replacement for the identification of medical claims. In addition, weinvestigate the impact of this method and compare it with 3 other dataaugmentation techniques, showing that the proposed method can result in asignificant (relative) improvement in the minority class.

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