@inproceedings{zhou-etal-2022-dual,
    title = "Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning",
    author = "Zhou, Jie  and
      Tian, Le  and
      Yu, Houjin  and
      Xiao, Zhou  and
      Su, Hui  and
      Zhou, Jie",
    editor = "Muresan, Smaranda  and
      Nakov, Preslav  and
      Villavicencio, Aline",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.findings-acl.8/",
    doi = "10.18653/v1/2022.findings-acl.8",
    pages = "79--84",
    abstract = "Prompt-based paradigm has shown its competitive performance in many NLP tasks. However, its success heavily depends on prompt design, and the effectiveness varies upon the model and training data. In this paper, we propose a novel dual context-guided continuous prompt (DCCP) tuning method. To explore the rich contextual information in language structure and close the gap between discrete prompt tuning and continuous prompt tuning, DCCP introduces two auxiliary training objectives and constructs input in a pair-wise fashion. Experimental results demonstrate that our method is applicable to many NLP tasks, and can often outperform existing prompt tuning methods by a large margin in the few-shot setting."
}