论文标题
几乎没有原型摊销条件随机场的射击事件检测
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
论文作者
论文摘要
当事件检测需要识别一些样本的新事件类型时，它往往会挣扎。先前的工作试图以识别分类的方式解决这个问题，但忽略了事件类型之间的触发差异，因此遭受了错误传播的困扰。在本文中，我们提出了一个新颖的统一模型，该模型将任务转换为使用双部分标签方案的几个弹奏标记问题。为此，我们首先提出了原型摊销条件随机场（PA-CRF），以模拟在几种镜头场景中的标签依赖性，该场景近似于基于标签原型的标签之间的过渡得分。然后引入了高斯分布来建模过渡分数，以减轻数据不足所产生的不确定估计。实验结果表明，统一模型比现有的识别模型更好地工作，而我们的PA-CRF进一步在基准数据集中实现了最佳结果。我们的代码和数据可在http://github.com/congxin95/pa-crf上找到。
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 http://github.com/congxin95/PA-CRF.
