用少量标注数据修复弱监督中的标签函数，提升标注质量。
Refining Labeling Functions with Limited Labeled Data
- 将标签函数建模为条件规则，仅微调其在少量标注样本上的输出。
- 修复后每个样本都有充分正确标签证据，且修复后的函数准确率显著提高。
- 适用于标注成本高、数据稀缺场景，尤其适合已有标签函数但需优化的用户。
程序化弱监督（PWS）通过组合用户提供的标签函数（LFs）在无标签数据上的输出，大幅降低人工标注成本。然而生成标签的质量直接依赖于LFs的准确性。本文研究如何利用少量标注样本修复LFs。为此，提出新方法：最小化修改已标注样本上的输出，使修复后的LFs满足两个条件：(i) 每个标注样本有足够正确的标签证据；(ii) 每个修复后的LF准确率足够高。将LFs建模为条件规则，实现对其选择性修正。实验表明，该系统在极小标注集下仍能有效提升LFs质量。
原文摘要 · Abstract (English)
Programmatic weak supervision (PWS) significantly reduces human effort for labeling data by combining the outputs of user-provided labeling functions (LFs) on unlabeled datapoints. However, the quality of the generated labels depends directly on the accuracy of the LFs. In this work, we study the problem of fixing LFs based on a small set of labeled examples. Towards this goal, we develop novel techniques for repairing a set of LFs by minimally changing their results on the labeled examples such that the fixed LFs ensure that (i) there is sufficient evidence for the correct label of each labeled datapoint and (ii) the accuracy of each repaired LF is sufficiently high. We model LFs as conditional rules which enables us to refine them, i.e., to selectively change their output for some inputs. We demonstrate experimentally that our system improves the quality of LFs based on surprisingly small sets of labeled datapoints.
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