Denoising Multi-Source Weak Supervision for Neural Text Classification

Understand

We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources.

• This problem is challenging because rule-induced weak labels are often noisy and incomplete.
• To address these two challenges, we design a label denoiser, which estimates the source reliability using a conditional soft attention mechanism and then reduces label noise by aggregating rule-annotated weak labels.
• The denoised pseudo labels then supervise a neural classifier to predicts soft labels for unmatched samples, which address the rule coverage issue.