@inproceedings{lei-etal-2023-tart,
    title = "{TART}: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation",
    author = "Lei, Shuo  and
      Zhang, Xuchao  and
      He, Jianfeng  and
      Chen, Fanglan  and
      Lu, Chang-Tien",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.617",
    doi = "10.18653/v1/2023.acl-long.617",
    pages = "11014--11026",
    abstract = "Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance. However, the performance of existing approaches heavily depends on the inter-class variance of the support set. As a result, it can perform well on tasks when the semantics of sampled classes are distinct while failing to differentiate classes with similar semantics. In this paper, we propose a novel Task-Adaptive Reference Transformation (TART) network, aiming to enhance the generalization by transforming the class prototypes to per-class fixed reference points in task-adaptive metric spaces. To further maximize divergence between transformed prototypes in task-adaptive metric spaces, TART introduces a discriminative reference regularization among transformed prototypes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, our model surpasses the state-of-the-art method by 7.4{\%} and 5.4{\%} in 1-shot and 5-shot classification on the 20 Newsgroups dataset, respectively.",
}

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance. However, the performance of existing approaches heavily depends on the inter-class variance of the support set. As a result, it can perform well on tasks when the semantics of sampled classes are distinct while failing to differentiate classes with similar semantics. In this paper, we propose a novel Task-Adaptive Reference Transformation (TART) network, aiming to enhance the generalization by transforming the class prototypes to per-class fixed reference points in task-adaptive metric spaces. To further maximize divergence between transformed prototypes in task-adaptive metric spaces, TART introduces a discriminative reference regularization among transformed prototypes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, our model surpasses the state-of-the-art method by 7.4{\%} and 5.4{\%} in 1-shot and 5-shot classification on the 20 Newsgroups dataset, respectively.
Lei, Shuo Zhang, Xuchao He, Jianfeng Chen, Fanglan Lu, Chang-Tien
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation