In many applications labeled data is not readily available, and needs to be collected via pain-staking human supervision.
 We propose a rule-exemplar model for collecting human supervision to combine the scalability 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.   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.
Coupled rule-exemplar supervision and a implication loss helps to jointly learn to denoise rules and imply labels.