@inproceedings{lai-etal-2021-learning,
    title = "Learning Prototype Representations Across Few-Shot Tasks for Event Detection",
    author = "Lai, Viet  and
      Dernoncourt, Franck  and
      Nguyen, Thien Huu",
    editor = "Moens, Marie-Francine  and
      Huang, Xuanjing  and
      Specia, Lucia  and
      Yih, Scott Wen-tau",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.emnlp-main.427/",
    doi = "10.18653/v1/2021.emnlp-main.427",
    pages = "5270--5277",
    abstract = "We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction. We propose to model the relations between training tasks in episodic few-shot learning by introducing cross-task prototypes. We further propose to enforce prediction consistency among classifiers across tasks to make the model more robust to outliers. Our extensive experiment shows a consistent improvement on three few-shot learning datasets. The findings suggest that our model is more robust when labeled data of novel event types is limited. The source code is available at \url{http://github.com/laiviet/fsl-proact}."
}