@inproceedings{ohashi-etal-2021-distinct,
    title = "Distinct Label Representations for Few-Shot Text Classification",
    author = "Ohashi, Sora  and
      Takayama, Junya  and
      Kajiwara, Tomoyuki  and
      Arase, Yuki",
    editor = "Zong, Chengqing  and
      Xia, Fei  and
      Li, Wenjie  and
      Navigli, Roberto",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2021.acl-short.105/",
    doi = "10.18653/v1/2021.acl-short.105",
    pages = "831--836",
    abstract = "Few-shot text classification aims to classify inputs whose label has only a few examples. Previous studies overlooked the semantic relevance between label representations. Therefore, they are easily confused by labels that are relevant. To address this problem, we propose a method that generates distinct label representations that embed information specific to each label. Our method is applicable to conventional few-shot classification models. Experimental results show that our method significantly improved the performance of few-shot text classification across models and datasets."
}