Darwin: Adaptive Rule Discovery for Labeling Text Data

There is consensus, especially in our current deep-learning era, that more training data almost always helps improve performance of our deep learning models. But the process of collecting labeled data remains a costly and cumbersome task. Naturally, researchers started looking into this problem, which has led to development of various techniques for reducing the labeling cost. Among these, is a popular technique called weak supervision, in which a collection of heuristics and rules are used to label the data. Of course, the labels would be noisy but these weak labels have proven to be valuable as long as the rules have a reasonable error rate.