DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning

• URL: http://arxiv.org/abs/2502.00305v1
• Date: Sat, 01 Feb 2025 04:00:03 GMT
• Title: DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
• Authors: Jiaxin Guo, C. L. Philip Chen, Shuzhen Li, Tong Zhang,
• Abstract summary: Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation. Existing CSAL methods overlook weak classes and hard representative examples, resulting in biased learning. This paper proposes a novel dual-diversity enhancing and uncertainty-aware framework for CSAL.
• Score: 54.35107462768146
• License:
• Abstract: Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for manual annotation. It provides high-quality data at a low annotation cost for label-scarce text classification. However, existing CSAL methods overlook weak classes and hard representative examples, resulting in biased learning. To address these issues, this paper proposes a novel dual-diversity enhancing and uncertainty-aware (DEUCE) framework for CSAL. Specifically, DEUCE leverages a pretrained language model (PLM) to efficiently extract textual representations, class predictions, and predictive uncertainty. Then, it constructs a Dual-Neighbor Graph (DNG) to combine information on both textual diversity and class diversity, ensuring a balanced data distribution. It further propagates uncertainty information via density-based clustering to select hard representative instances. DEUCE performs well in selecting class-balanced and hard representative data by dual-diversity and informativeness. Experiments on six NLP datasets demonstrate the superiority and efficiency of DEUCE.

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