Semi-Supervised Data Programming with Subset Selection

TL;DR

This paper introduces a semi-supervised data programming framework called extbackslash model that combines rule-based labeling with subset selection to improve text classification, outperforming existing methods.

Contribution

The work presents a novel semi-supervised data programming paradigm and a subset selection method to enhance label efficiency and model performance in text classification tasks.

Findings

Significant performance improvements over state-of-the-art on seven datasets.

Effective combination of semi-supervision, data programming, and subset selection.

Subset selection enhances the complementarity between labeled data and labeling functions.

Abstract

The paradigm of data programming, which uses weak supervision in the form of rules/labelling functions, and semi-supervised learning, which augments small amounts of labelled data with a large unlabelled dataset, have shown great promise in several text classification scenarios. In this work, we argue that by not using any labelled data, data programming based approaches can yield sub-optimal performances, particularly when the labelling functions are noisy. The first contribution of this work is an introduction of a framework, \model which is a semi-supervised data programming paradigm that learns a \emph{joint model} that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space. Next, we also study \modelss which additionally does subset selection on top of the joint semi-supervised data programming objective and \emph{selects} a set of examples that can be used as the labelled set by \model. The goal of \modelss is to ensure that the labelled data can \emph{complement} the labelling functions, thereby benefiting from both data-programming as well as appropriately selected data for human labelling. We demonstrate that by effectively combining semi-supervision, data-programming, and subset selection paradigms, we significantly outperform the current state-of-the-art on seven publicly available datasets. \footnote{The source code is available at \url{https://github.com/ayushbits/Semi-Supervised-LFs-Subset-Selection}}