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Mixup is an effective data augmentation method that generates new augmentedsamples by aggregating linear combinations of different original samples.However, if there are noises or aberrant features in the original samples,Mixup may propagate them to the augmented samples, leading to over-sensitivityof the model to these outliers . To solve this problem, this paper proposes anew Mixup method called AMPLIFY. This method uses the Attention mechanism ofTransformer itself to reduce the influence of noises and aberrant values in theoriginal samples on the prediction results, without increasing additionaltrainable parameters, and the computational cost is very low, thereby avoidingthe problem of high resource consumption in common Mixup methods such asSentence Mixup . The experimental results show that, under a smallercomputational resource cost, AMPLIFY outperforms other Mixup methods in textclassification tasks on 7 benchmark datasets, providing new ideas and new waysto further improve the performance of pre-trained models based on the Attentionmechanism, such as BERT, ALBERT, RoBERTa, and GPT. Our code can be obtained athttps://github.com/kiwi-lilo/AMPLIFY.

AMPLIFY:Attention-based Mixup for Performance Improvement and Label Smoothing in Transformer