DEUCE : Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning (2024)

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.

## introduction cold-start active learning (csal; @xcite has gained much attention for efficiently labeling large corpora from zero. given an unlabeled corpus (i.e., the ''coldstart'' stage), it aims to acquire a small subset (seed set) for annotation. such absence of labels can happen due to data privacy concerns (holzinger, * tong zhang is the corresponding author. 2016; @xcite , limited domain experts foot_0 @xcite , labeling difficulty @xcite , quick expiration of labels @xcite , etc. in real-world tasks with specialized domains (e.g., medical report classification with rare diseases; de @xcite , the complete absence of labels and lack of a posteriori knowledge pose challenges to csal. while active learning (al) has been studied for a wide range of nlp tasks @xcite , the cold-start problem has been hardly addressed. at the cold-start stage, the model is untrained and no labeled data are available for validation. traditional csal applies random sampling @xcite , diversity sampling @xcite , or uncertainty sampling @xcite . however, random sampling suffers from high variance @xcite ; diversity sampling is prone to easy examples and vector space noise @xcite ; and uncertainty sampling is prone to redundant examples, outliers, and unreliable metrics @xcite . moreover, existing methods ignore class diversity, where the sampling bias often results in class imbalance @xcite . at worst, the missed cluster effect @xcite can happen, i.e., clusters of weak classes are neglected. @xcite showed that an unrepresentative seed set gives rise to this effect. learning is misguided, if started unfavorably. the key challenge for csal lies in how to acquire a diverse and informative seed set. as a general heuristic @xcite , a proper seed set should strike a balance between exploring the input space for instance regions (e.g., diversity sampling) and exploiting the version space for decision boundaries (e.g., uncertainty sampling). such hybrid csal strategies have been proposed based on combinations of neighbor-awareness @xcite @xcite , clustering @xcite @xcite @xcite , and uncertainty estimation @xcite @xcite . however, existing methods fail to explore the label space to enhance class diversity and mitigate imbalance. moreover, most methods perform diversity sampling followed by uncertainty sampling, treating both aspects in isolation. to address these challenges, this paper presents deuce, a dual-diversity enhancing and uncertainty-aware framework for csal. it adopts a graph-based hybrid strategy to enhance diversity and informativeness. different from previous works, deuce not only emphasizes the diversity in textual contents (textual diversity), but also diversity in class predictions (class diversity). this is termed dual-diversity in this paper. to achieve this in the cold-start stage, it exploits the rich representational and predictive capabilities of plms. for informativeness, the predictive uncertainty is estimated from a one-vs-all (ova) perspective. this helps mining informative ''hard examples'' for learning. then, deuce further employs manifold learning techniques @xcite to derive dual-diversity information. this results in the novel construction of a dual-neighbor graph (dng). finally, deuce performs densitybased uncertainty propagation and farthest point sampling (fps) on the dng. while propagation prioritizes representatively uncertain (ru) instances, fps enhances the dual-diversity. overall, deuce ensures a more diverse and informative acquisition. the merits of deuce are attributed to the following contributions: ## methodology in this section, the methodology of the proposed deuce is introduced. section 3.1 first defines csal and declares the notations for the rest of this paper. the framework of deuce is then elaborated in section 3.2.