Empowering Active Learning to Jointly Optimize System and User Demands

9 May 2020Ji-Ung LeeChristian M. MeyerIryna Gurevych

Existing approaches to active learning maximize the system performance by sampling unlabeled instances for annotation that yield the most efficient training. However, when active learning is integrated with an end-user application, this can lead to frustration for participating users, as they spend time labeling instances that they would not otherwise be interested in reading... (read more)

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