The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming
This work addresses the challenge of data labeling costs for machine learning practitioners, but it is incremental as it primarily surveys and applies existing methods.
The paper tackles the problem of reducing reliance on expensive point-by-point labeled data by surveying and analyzing the Data Programming framework, which uses noisy heuristics to assign probabilistic labels, and demonstrates its application on two real-world text classification tasks.
Most advanced supervised Machine Learning (ML) models rely on vast amounts of point-by-point labelled training examples. Hand-labelling vast amounts of data may be tedious, expensive, and error-prone. Recently, some studies have explored the use of diverse sources of weak supervision to produce competitive end model classifiers. In this paper, we survey recent work on weak supervision, and in particular, we investigate the Data Programming (DP) framework. Taking a set of potentially noisy heuristics as input, DP assigns denoised probabilistic labels to each data point in a dataset using a probabilistic graphical model of heuristics. We analyze the math fundamentals behind DP and demonstrate the power of it by applying it on two real-world text classification tasks. Furthermore, we compare DP with pointillistic active and semi-supervised learning techniques traditionally applied in data-sparse settings.