DEUCE: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
This addresses the issue of biased data selection in label-scarce text classification for NLP practitioners, though it is incremental as it builds on existing active learning methods.
The paper tackles the problem of biased learning in cold-start active learning for text classification by proposing the DEUCE framework, which enhances dual-diversity and uncertainty-awareness to select class-balanced and hard representative instances, achieving superior performance on six NLP datasets.
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.