@inproceedings{wang-etal-2021-learning-language-description,
    title = "Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework",
    author = "Wang, Yaqing  and
      Chu, Haoda  and
      Zhang, Chao  and
      Gao, Jing",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.findings-emnlp.139/",
    doi = "10.18653/v1/2021.findings-emnlp.139",
    pages = "1618--1630",
    abstract = "In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings. Built upon large-scale pre-trained language models, we propose a novel NER framework, namely SpanNER, which learns from natural language supervision and enables the identification of never-seen entity classes without using in-domain labeled data. We perform extensive experiments on 5 benchmark datasets and evaluate the proposed method in the few-shot learning, domain transfer and zero-shot learning settings. The experimental results show that the proposed method can bring 10{\%}, 23{\%} and 26{\%} improvements in average over the best baselines in few-shot learning, domain transfer and zero-shot learning settings respectively."
}