Instead of updating all labels for each example, we divide labels into three bins (general, fine, and ultra-fine), and update labels only in bin containing at least one positive label.
the training objective is to minimize J where t is the target vector at each granularity: Where 1 category (t) is an indicator function that checks if t contains a type in the category, and   J category is the category-specific logistic regression objective: 5 Evaluation Experiment Setup The crowdsourced dataset (Section 2.1) was randomly split into train, development, and test sets, each with about 2,000 examples.