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 fewshot learning datasets. The findings suggest that

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... model is more robust when labeled data of novel event types is limited. The source code is available at http://github.com/ laiviet/fsl-proact.