Scarcity of labeled data is a bottleneck for supervised learning models. A paradigm that has evolved for dealing with this problem is data programming. An existing data programming paradigm allows human supervision to be provided as a set of discrete labeling functions (LF) that output possibly noisy labels to input instances and a generative modelfor consolidating the weak labels. We enhance and generalize this paradigm by supporting functions that output a continuous score (instead of a hard label) that noisily correlates with labels. We show across five applications that continuous LFs are more natural to program and lead to improved recall. We also show that accuracy of existing generative models is unstable with respect to initialization, training epochs, and learning rates. We give control to the data programmer to guide the training process by providing intuitive quality guides with each LF. We propose an elegant method of incorporating these guides into the generative model. Our overall method, called CAGE, makes the data programming paradigm more reliable than other tricks based on initialization, sign-penalties, or soft-accuracy constraints.

该论文提出了一种名为CAGE的方法，它是一种基于数据编程范 Paradigm 的算法。它通过支持输出与标签不确定相关的连续分数的函数，以增强和推广现有的 data programming 范 Paradigm，使程序更加自然且具有更好的召回率，并可以通过整合人类专家的质量指南来引导训练过程，从而使算法更加可靠。

基于连续和质量引导标记函数的数据编程