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.

该研究提出了一种 Prototypical Amortized Conditional Random Field（PA-CRF）模型，用于近似标签原型之间的转移分数来建模字词依赖性，同时引入高斯分布模型来缓解模型对不充足数据的不确定性，从而解决事件类型识别中样本数量不足导致的错误传播问题，实验结果表明，该方法优于传统的基于识别-分类的方法。

基于原型化摊余条件随机场的少样本事件检测