Perturbation-based Active Learning for Question Answering
Fan Luo Mihai Surdeanu
Building a question answering (QA) model with less annotation costs can be
achieved by utilizing active learning (AL) training strategy. It selects the
most informative unlabeled training data to update the model effectively.
Acquisition functions for AL are used to determine how informative each
training example is, such as uncertainty or diversity based sampling. In this
work, we propose a perturbation-based active learning acquisition strategy and
demonstrate it is more effective than existing commonly used strategies.