Journal:Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI), CCF-A
Abstract:Data augmentation is widely used in text classification, especially in the low-resource regime where a few examples for each class are available during training. Despite the success, generating data augmentations as hard positive examples that may increase their effectiveness is under-explored. This paper proposes an Adversarial Word Dilution (AWD) method that can generate hard positive examples as text data augmentations to train the low-resource text classification model efficiently. Our idea of augmenting the text data is to dilute the embedding of strong positive words by weighted mixing with unknown-word embedding, making the augmented inputs hard to be recognized as positive by the classification model. We adversarially learn the dilution weights through a constrained min-max optimization process with the guidance of the labels. Empirical studies on three benchmark datasets show that AWD can generate more effective data augmentations and outperform the state-of-the-art text data augmentation methods. The additional analysis demonstrates that the data augmentations generated by AWD are interpretable and can flexibly extend to new examples without further training.
Co-author:Junfan Chen,Richong Zhang, Zheyan Luo,Chunming Hu, Yongyi Mao
Indexed by:国际学术会议
Page Number:12626-12634
Translation or Not:no
Date of Publication:2023-01-01