TL;DR本文提出了一种自适应缩放算法，通过动态代价敏感性学习实现直接优化 F 值，处理信息提取中稀疏正实例的问题，并采用边际效用的概念对实例重要性进行测量，在不引入额外超参数的情况下，有效解决了神经网络检测模型训练不稳定性的问题。
Abstract
This paper focuses on detection tasks in information extraction, where positive instances are sparsely distributed and models are usually evaluated using F-measure on positive classes. These characteristics often