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Perturbation-based Active Learning for Question Answering

arXiv 2311.02345 ↗First posted 4 Nov 2023cs.CL cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 106 of its 206 references that are in the map. First posted on arXiv 4 Nov 2023.

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