论文标题
较弱的监督量估计量的量级准确性估算
Weak Supervision with Incremental Source Accuracy Estimation
论文作者
论文摘要
由渴望生成标签的实时数据的愿望的动机，我们开发了一种方法来逐步估计弱监督源的依赖性结构和准确性。我们的方法首先估计与监督源相关的依赖关系结构，然后在接收新数据时使用它来迭代更新估计的源精度。使用使用公共可用数据集训练的两个现成的分类模型和启发式功能作为监督来源，我们表明我们的方法生成了概率标签，其准确性匹配现有的离线方法。
Motivated by the desire to generate labels for real-time data we develop a method to estimate the dependency structure and accuracy of weak supervision sources incrementally. Our method first estimates the dependency structure associated with the supervision sources and then uses this to iteratively update the estimated source accuracies as new data is received. Using both off-the-shelf classification models trained using publicly-available datasets and heuristic functions as supervision sources we show that our method generates probabilistic labels with an accuracy matching that of existing off-line methods.
