# Label Augmentation with Reinforced Labeling for Weak Supervision

arXiv: https://arxiv.org/abs/2204.06436 · HTML: https://mlsanity.com/paper/2204.06436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 46 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 30 of 32)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Training Complex Models with Multi-Task Weak Supervision](https://mlsanity.com/paper/1810.02840/) (2018)
- [Implicit Semantic Data Augmentation for Deep Networks](https://mlsanity.com/paper/1909.12220/) (2019)
- [Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods](https://mlsanity.com/paper/2002.11955/) (2020)
- [Who Said What: Modeling Individual Labelers Improves Classification](https://mlsanity.com/paper/1703.08774/) (2017)
- [Neural Ranking Models with Weak Supervision](https://mlsanity.com/paper/1704.08803/) (2017)
- [Learning from Rules Generalizing Labeled Exemplars](https://mlsanity.com/paper/2004.06025/) (2020)
- [Training Classifiers with Natural Language Explanations](https://mlsanity.com/paper/1805.03818/) (2018)
- [Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale](https://mlsanity.com/paper/1812.00417/) (2018)
- [Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling](https://mlsanity.com/paper/2012.06046/) (2020)
- [Augmenting Neural Networks with First-order Logic](https://mlsanity.com/paper/1906.06298/) (2019)
- [Learning the Structure of Generative Models without Labeled Data](https://mlsanity.com/paper/1703.00854/) (2017)
- [A Bayesian Data Augmentation Approach for Learning Deep Models](https://mlsanity.com/paper/1710.10564/) (2017)
- [Learning From Noisy Singly-labeled Data](https://mlsanity.com/paper/1712.04577/) (2017)
- [Self-Training with Weak Supervision](https://mlsanity.com/paper/2104.05514/) (2021)
- [Denoising Multi-Source Weak Supervision for Neural Text Classification](https://mlsanity.com/paper/2010.04582/) (2020)
- [Learning Dependency Structures for Weak Supervision Models](https://mlsanity.com/paper/1903.05844/) (2019)
- [Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach](https://mlsanity.com/paper/1705.07086/) (2017)
- [NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction](https://mlsanity.com/paper/1909.02177/) (2019)
- [Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices](https://mlsanity.com/paper/1909.06349/) (2019)
- [Fidelity-Weighted Learning](https://mlsanity.com/paper/1711.02799/) (2017)
- [GOGGLES: Automatic Image Labeling with Affinity Coding](https://mlsanity.com/paper/1903.04552/) (2019)
- [Inferring Generative Model Structure with Static Analysis](https://mlsanity.com/paper/1709.02477/) (2017)
- [Adversarial Label Learning](https://mlsanity.com/paper/1805.08877/) (2018)
- [Knodle: Modular Weakly Supervised Learning with PyTorch](https://mlsanity.com/paper/2104.11557/) (2021)
- [Bootstrapping Conversational Agents With Weak Supervision](https://mlsanity.com/paper/1812.06176/) (2018)
- [Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated Labeled Data](https://mlsanity.com/paper/1803.05137/) (2018)
- [Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings](https://mlsanity.com/paper/2006.15168/) (2020)
- [Learning Structured Representations of Entity Names using Active Learning and Weak Supervision](https://mlsanity.com/paper/2011.00105/) (2020)

## Built on by (most cited first, 1 of 1)

- [Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets](https://mlsanity.com/paper/2309.06503/) (2023)

## Abstract (arXiv)

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.