Semi-Supervised Data Programming with Subset Selection
Ayush Maheshwari, Oishik Chatterjee, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer · 2021
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, SPEAR which is a semi-supervised data programming paradigm that learns a joint model that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space.Next, we also study SPEAR-SS which additionally does subset selection on top of the joint semi-supervised data programming objective and selects a set of examples that can be used as the labelled set by SPEAR.The goal of SPEAR-SS is to ensure that the labelled data can 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. 1