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.
@article{arxiv.2502.00305,
title = {DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning},
author = {Jiaxin Guo and C. L. Philip Chen and Shuzhen Li and Tong Zhang},
journal= {arXiv preprint arXiv:2502.00305},
year = {2025}
}
Comments
18 pages, 3 figures, 12 tables. Accepted manuscript by TACL. For published version by MIT Press, see https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00731/125950