@inproceedings{pham-etal-2025-mitigating,
    title = "Mitigating Non-Representative Prototypes and Representation Bias in Few-Shot Continual Relation Extraction",
    author = "Pham, Thanh Duc  and
      Hai, Nam Le  and
      Van, Linh Ngo  and
      Diep, Nguyen Thi Ngoc  and
      Dinh, Sang  and
      Nguyen, Thien Huu",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.530/",
    pages = "10791--10809",
    ISBN = "979-8-89176-251-0",
    abstract = "To address the phenomenon of similar classes, existing methods in few-shot continual relation extraction (FCRE) face two main challenges: non-representative prototypes and representation bias, especially when the number of available samples is limited. In our work, we propose Minion to address these challenges. Firstly, we leverage the General Orthogonal Frame (GOF) structure, based on the concept of Neural Collapse, to create robust class prototypes with clear separation, even between analogous classes. Secondly, we utilize label description representations as global class representatives within the fast-slow contrastive learning paradigm. These representations consistently encapsulate the essential attributes of each relation, acting as global information that helps mitigate overfitting and reduces representation bias caused by the limited local few-shot examples within a class. Extensive experiments on well-known FCRE benchmarks show that our method outperforms state-of-the-art approaches, demonstrating its effectiveness for advancing RE system."
}