Label Augmentation with Reinforced Labeling for Weak Supervision
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 existi...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
13-04-2022
|
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| DOI: | 10.48550/arxiv.2204.06436 |