DEUCE提升冷启动主动学习的样本选择质量，避免类别偏差。
DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
- 用双多样性图融合文本与类别多样性，保证数据分布均衡
- 通过密度聚类传播不确定性，选出难样本代表
- 在6个NLP数据集上验证有效，适合标注资源少的场景
冷启动主动学习（CSAL）从无标签数据中选取有价值样本进行人工标注，以低成本获取高质量数据，适用于标签稀缺的文本分类任务。然而，现有方法忽视弱类别和难代表性样本，导致学习偏差。本文提出一种新的双多样性增强与不确定性感知框架（DEUCE）。DEUCE利用预训练语言模型（PLM）高效提取文本表征、类别预测及预测不确定性；构建双邻域图（DNG），融合文本多样性和类别多样性信息，确保数据分布平衡；进一步通过基于密度的聚类传播不确定性，筛选出难代表性样本。实验在6个NLP数据集上表明，DEUCE能有效选择类别均衡且具有代表性的难样本，兼具优越性与高效性。
原文摘要 · Abstract (English)
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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