  Recently, several studies have investigated active learning (AL) for natural
language processing tasks to alleviate data dependency. However, for query
selection, most of these studies mainly rely on uncertainty-based sampling,
which generally does not exploit the structural information of the unlabeled
data. This leads to a sampling bias in the batch active learning setting, which
selects several samples at once. In this work, we demonstrate that the amount
of labeled training data can be reduced using active learning when it
incorporates both uncertainty and diversity in the sequence labeling task. We
examined the effects of our sequence-based approach by selecting weighted
diverse in the gradient embedding approach across multiple tasks, datasets,
models, and consistently outperform classic uncertainty-based sampling and
diversity-based sampling.
