  Within the scope of natural language processing, the domain of multi-label
text classification is uniquely challenging due to its expansive and uneven
label distribution. The complexity deepens due to the demand for an extensive
set of annotated data for training an advanced deep learning model, especially
in specialized fields where the labeling task can be labor-intensive and often
requires domain-specific knowledge. Addressing these challenges, our study
introduces a novel deep active learning strategy, capitalizing on the Beta
family of proper scoring rules within the Expected Loss Reduction framework. It
computes the expected increase in scores using the Beta Scoring Rules, which
are then transformed into sample vector representations. These vector
representations guide the diverse selection of informative samples, directly
linking this process to the model's expected proper score. Comprehensive
evaluations across both synthetic and real datasets reveal our method's
capability to often outperform established acquisition techniques in
multi-label text classification, presenting encouraging outcomes across various
architectural and dataset scenarios.
