Papers with acquisition function

2 papers
Active Learning by Acquiring Contrastive Examples (2021.emnlp-main)

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Challenge: Using uncertainty and diversity sampling, active learning acquisition functions select difficult and diverse data points from a pool of unlabeled data.
Approach: They propose an active learning acquisition function that selects contrastive examples from unlabeled data.
Outcome: The proposed approach performs better or equal to the best performing baseline on all tasks, on both in-domain and out-of-domain data.
ALLSH: Active Learning Guided by Local Sensitivity and Hardness (2022.findings-naacl)

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Challenge: Existing studies show that labeling in crowdsourcing annotations is not an annotation artifact but rather a core linguistic phenomenon.
Approach: They propose to retrieve unlabeled data with a local sensitivity and hardness-aware acquisition function.
Outcome: The proposed method achieves consistent gains over the commonly used active learning strategies in various classification tasks.

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