Learning from Rules Generalizing Labeled Exemplars

In many applications labeled data is not readily available, and needs to be collected via painstaking 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. With the ever-increasing reach of machine learning, a common hurdle to new adoptions is the lack of labeled data and the painstaking process involved in collecting human supervision. Over the years, several strategies have evolved.