Interactive learning with proactive cognition enhancement for crowd workers

Learning from crowds often performs in an active learning paradigm, aiming to improve learning performance quickly as well as to reduce labeling cost by selecting proper workers to (re)label critical instances. Previous active learning methods for learning from crowds do not have any proactive mechanism to effectively improve the reliability of workers, which prevents to obtain steadily rising learning curves. To help workers improve their reliability while performing tasks, this paper proposes a novel Interactive Learning framework with Proactive Cognitive Enhancement (ILPCE) for crowd workers. The ILPCE framework includes an interactive learning mechanism: When crowd workers perform labeling tasks in active learning, their cognitive ability to the specific domain can be enhanced through learning the exemplars selected by a psychological model-based machine teaching method. A novel probabilistic truth inference model and an interactive labeling scheme are proposed to ensure the effectiveness of the interactive learning mechanism and the performance of learning models can be simultaneously improved through a fast and low-cost way. Experimental results on three real-world learning tasks demonstrate that our ILPCE significantly outperforms five representative state-of-the-art methods.