Learning from Rules Generalizing Labeled Exemplars

TL;DR

This paper introduces a rule-exemplar method that efficiently combines rule-based supervision with labeled exemplars, improving learning accuracy in scenarios with limited or noisy labeled data.

Contribution

It presents a novel training algorithm that jointly denoises rules and trains models using a soft implication loss, enhancing learning from noisy supervision.

Findings

Outperforms existing methods on five tasks

Effectively denoises rules through coupled supervision

Improves accuracy with noisy and limited supervision

Abstract

In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision. We propose a rule-exemplar method for collecting human supervision to combine the efficiency of rules with the quality of instance labels. The supervision is coupled such that it is both natural for humans and synergistic for learning. We propose a training algorithm that jointly denoises rules via latent coverage variables, and trains the model through a soft implication loss over the coverage and label variables. The denoised rules and trained model are used jointly for inference. Empirical evaluation on five different tasks shows that (1) our algorithm is more accurate than several existing methods of learning from a mix of clean and noisy supervision, and (2) the coupled rule-exemplar supervision is effective in denoising rules.