• Extensive experiments on real-world data sets demonstrate that the effectiveness of our proposed approach consistently outperforms existing methods.
the macro-F1 score can be increased from 0.78 to 0.92 by assigning 25% of the labeling work to human experts in a 20-class text classification task.