@inproceedings{hu-etal-2025-towards,
    title = "Towards Robust Few-Shot Relation Classification: Incorporating Relation Description with Agreement",
    author = "Hu, Mengting  and
      Wu, Jianfeng  and
      Jiang, Ming  and
      Xie, Yalan  and
      Wang, Zhunheng  and
      Ying, Rui  and
      Liu, Xiaoyi  and
      Xu, Ruixuan  and
      Gao, Hang  and
      Cheng, Renhong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.442/",
    doi = "10.18653/v1/2025.findings-emnlp.442",
    pages = "8335--8349",
    ISBN = "979-8-89176-335-7",
    abstract = "Few-shot relation classification aims to recognize the relation between two mentioned entities, with the help of only a few support samples. However, a few samples tend to be limited for tackling unlimited queries. If a query cannot find references from the support samples, it is defined as none-of-the-above (NOTA). Previous works mainly focus on how to distinguish $N+1$ categories, including $N$ known relations and one NOTA class, to accurately recognize relations. However, the robustness towards various NOTA rates, i.e. the proportion of NOTA among queries, is under investigation. In this paper, we target the robustness and propose a simple but effective framework. Specifically, we introduce relation descriptions as external knowledge to enhance the model{'}s comprehension of the relation semantics. Moreover, we further promote robustness by proposing a novel agreement loss. It is designed for seeking decision consistency between the instance-level decision, i.e. support samples, and relation-level decision, i.e. relation descriptions. Extensive experimental results demonstrate that the proposed framework outperforms strong baselines while being robust against various NOTA rates. The code is released on GitHub at https://github.com/Pisces-29/RoFRC."
}