Label Augmentation with Reinforced Labeling for Weak Supervision

TL;DR

This paper introduces reinforced labeling, a method that enhances weak supervision by augmenting labeling functions with similarity-based inference, significantly improving classifier performance across multiple domains.

Contribution

It proposes a novel reinforced labeling approach that leverages data features and similarities to increase labeling coverage in weak supervision, addressing limitations of existing data programming methods.

Findings

Up to +21 points in accuracy

Up to +61 points in F1 scores

Effective across diverse domains

Abstract

Weak supervision (WS) is an alternative to the traditional supervised learning to address the need for ground truth. Data programming is a practical WS approach that allows programmatic labeling data samples using labeling functions (LFs) instead of hand-labeling each data point. However, the existing approach fails to fully exploit the domain knowledge encoded into LFs, especially when the LFs' coverage is low. This is due to the common data programming pipeline that neglects to utilize data features during the generative process. This paper proposes a new approach called reinforced labeling (RL). Given an unlabeled dataset and a set of LFs, RL augments the LFs' outputs to cases not covered by LFs based on similarities among samples. Thus, RL can lead to higher labeling coverage for training an end classifier. The experiments on several domains (classification of YouTube comments, wine quality, and weather prediction) result in considerable gains. The new approach produces significant performance improvement, leading up to +21 points in accuracy and +61 points in F1 scores compared to the state-of-the-art data programming approach.