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Within the scope of natural language processing, the domain of multi-labeltext classification is uniquely challenging due to its expansive and unevenlabel distribution. The complexity deepens due to the demand for an extensiveset of annotated data for training an advanced deep learning model, especiallyin specialized fields where the labeling task can be labor-intensive and oftenrequires domain-specific knowledge. Addressing these challenges, our studyintroduces a novel deep active learning strategy, capitalizing on the Betafamily of proper scoring rules within the Expected Loss Reduction framework. Itcomputes the expected increase in scores using the Beta Scoring Rules, whichare then transformed into sample vector representations. These vectorrepresentations guide the diverse selection of informative samples, directlylinking this process to the model's expected proper score. Comprehensiveevaluations across both synthetic and real datasets reveal our method'scapability to often outperform established acquisition techniques inmulti-label text classification, presenting encouraging outcomes across variousarchitectural and dataset scenarios.

Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification