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.

本研究介绍了一种新颖的深度主动学习策略，利用期望损失减少框架中的 Beta 系列合适评分规则来解决自然语言处理范围内多标签文本分类的挑战。通过计算预期分数的增加量，并将其转化为样本向量表示，该方法在各种架构和数据集情景中通常胜过传统的获取技术，在多标签文本分类中表现出令人鼓舞的结果。