Recent Weak Supervision (WS) approaches have had widespread success in easing
the bottleneck of labeling training data for machine learning by synthesizing
labels from multiple potentially noisy supervision sources. However, proper
measurement and analysis of these approaches remain a challenge. First,
datasets used in existing works are often private and/or custom, limiting
standardization. Second, WS datasets with the same name and base data often
vary in terms of the labels and weak supervision sources used, a significant
"hidden" source of evaluation variance. Finally, WS studies often diverge in
terms of the evaluation protocol and ablations used. To address these problems,
we introduce a benchmark platform, WRENCH, for thorough and standardized
evaluation of WS approaches. It consists of 22 varied real-world datasets for
classification and sequence tagging; a range of real, synthetic, and
procedurally-generated weak supervision sources; and a modular, extensible
framework for WS evaluation, including implementations for popular WS methods.
We use WRENCH to conduct extensive comparisons over more than 120 method
variants to demonstrate its efficacy as a benchmark platform. The code is
available at this https URL

本文提出了一种名为 WRENCH 的基准平台，用于对弱监督方法的评估和比较，它包括用于分类和序列标记的 22 个真实世界数据集，一系列真实、合成和过程生成的弱监督源，并提供流行的弱监督方法的实施。