  Methods for improving the training and prediction quality of weakly
supervised machine learning models vary in how much they are tailored to a
specific task, or integrated with a specific model architecture. In this work,
we propose a software framework Knodle that provides a modularization for
separating weak data annotations, powerful deep learning models, and methods
for improving weakly supervised training. This modularization gives the
training process access to fine-grained information such as data set
characteristics, matches of heuristic rules, or elements of the deep learning
model ultimately used for prediction. Hence, our framework can encompass a wide
range of training methods for improving weak supervision, ranging from methods
that only look at the correlations of rules and output classes (independently
of the machine learning model trained with the resulting labels), to those
methods that harness the interplay of neural networks and weakly labeled data.
