@inproceedings{rahamim-etal-2023-text,
    title = "Text Augmentation Using Dataset Reconstruction for Low-Resource Classification",
    author = "Rahamim, Adir  and
      Uziel, Guy  and
      Goldbraich, Esther  and
      Anaby Tavor, Ateret",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.466",
    doi = "10.18653/v1/2023.findings-acl.466",
    pages = "7389--7402",
    abstract = "In the deployment of real-world text classification models, label scarcity is a common problem and as the number of classes increases, this problem becomes even more complex. An approach to addressing this problem is by applying text augmentation methods. One of the more prominent methods involves using the text-generation capabilities of language models. In this paper, we propose Text AUgmentation by Dataset Reconstruction (TAU-DR), a novel method of data augmentation for text classification. We conduct experiments on several multi-class datasets, showing that our approach improves the current state-of-the-art techniques for data augmentation.",
}

In the deployment of real-world text classification models, label scarcity is a common problem and as the number of classes increases, this problem becomes even more complex. An approach to addressing this problem is by applying text augmentation methods. One of the more prominent methods involves using the text-generation capabilities of language models. In this paper, we propose Text AUgmentation by Dataset Reconstruction (TAU-DR), a novel method of data augmentation for text classification. We conduct experiments on several multi-class datasets, showing that our approach improves the current state-of-the-art techniques for data augmentation.
Rahamim, Adir Uziel, Guy Goldbraich, Esther Anaby Tavor, Ateret
Text Augmentation Using Dataset Reconstruction for Low-Resource Classification