Perturbation-based Active Learning for Question Answering

• URL: http://arxiv.org/abs/2311.02345v1
• Date: Sat, 4 Nov 2023 08:07:23 GMT
• Title: Perturbation-based Active Learning for Question Answering
• Authors: Fan Luo, Mihai Surdeanu
• Abstract summary: 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.
• Score: 25.379528163789082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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