Event Detection, a fundamental task of Information Extraction, tends to struggle when it needs to recognize novel event types with a few samples , i. e. Few-Shot Event Detection (FSED). Previous identify-then-classify paradigm attempts to solve this problem in the pipeline manner but ignores the trigger discrepancy between event types, thus suffering from the error propagation. In this paper, we present a novel unified joint model which converts the task to a few-shot tagging problem with a double-part tagging scheme. To this end, we first design the Prototypical Amortized Conditional Random Field (PA-CRF) to model the label dependency in the few-shot scenario, which builds prototypical amortization networks to approximate the transition scores between labels based on the label prototypes. Then Gaussian distribution is introduced for the modeling of the transition scores in PA-CRF to alleviate the uncertain estimation resulting from insufficient data. We conduct experiments on the benchmark dataset FewEvent and the experimental results show that the tagging based methods are better than existing pipeline and joint learning methods. In addition, the proposed PA-CRF  achieves the best results on the public dataset .
Event detection tends to struggle when it needs to recognize novel event types with a few samples . The previous work attempts to solve this problem in the identify-then-classify manner but ignores the trigger discrepancy between event types, thus suffering from the error propagation. In this paper, we present a novel unified model which converts the task to a few-shot tagging problem with a double-part tagging scheme. To this end, we first propose the Prototypical Amortized Conditional Random Field (PA-CRF) to model the label dependency in the few-shot scenario, which approximates the transition scores between labels based on the label prototypes. Then Gaussian distribution is introduced for modeling of the transition scores to alleviate the uncertain estimation resulting from insufficient data. Experimental results show that the unified models work better than existing identify-then-classify models and our PA-CRF further achieves the best results on the benchmark dataset FewEvent. Our code and data are available at URL
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