Our framework can also incorporate crowd knowledge easily, in which the teachers can be human crowds, and then the NITE can employs active learning (Olsson, 2009) or lifelong machine learning (Chen and Liu, 2016) to progressively polishing the student model.
due to insufficient labeled training data, deep models usually fail to behave normally in such domain, and state-of-the-art methods in these domains are usually dominated by rule based deductive methods or shallow model with hand-crafted features.