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Event detection tends to struggle when it needs to recognize novel eventtypes with a few samples. The previous work attempts to solve this problem inthe identify-then-classify manner but ignores the trigger discrepancy betweenevent types, thus suffering from the error propagation. In this paper, wepresent a novel unified model which converts the task to a few-shot taggingproblem with a double-part tagging scheme. To this end, we first propose thePrototypical Amortized Conditional Random Field (PA-CRF) to model the labeldependency in the few-shot scenario, which approximates the transition scoresbetween labels based on the label prototypes. Then Gaussian distribution isintroduced for modeling of the transition scores to alleviate the uncertainestimation resulting from insufficient data. Experimental results show that theunified models work better than existing identify-then-classify models and ourPA-CRF further achieves the best results on the benchmark dataset FewEvent. Ourcode and data are available at http://github.com/congxin95/PA-CRF.

Few-Shot Event Detection with Prototypical Amortized Conditional Random Field