在多标签文本分类的深度主动学习中利用 Beta 评分的力量
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification
AI总结：
本文提出一种基于 Beta 评分规则和期望损失减少框架的深度主动学习方法，通过样本向量表示实现多样化信息样本选择，在多标签文本分类中优于现有采集技术。
摘要：
在自然语言处理领域，多标签文本分类因其广泛且不均衡的标签分布而具有独特的挑战性。由于训练先进深度学习模型需要大量标注数据，这一复杂性进一步加深，尤其是在标注任务劳动密集且通常需要领域特定知识的专业领域中。为应对这些挑战，本研究引入了一种新颖的深度主动学习策略，在期望损失减少框架内利用 Beta 族适当评分规则。该方法使用 Beta 评分规则计算期望得分增量，并将其转换为样本向量表示。这些向量表示引导信息性样本的多样化选择，从而将该过程与模型的期望适当得分直接关联起来。在合成数据集和真实数据集上的综合评估表明，我们的方法在多标签文本分类中通常能够优于已有的采集技术，并在多种架构和数据集场景下展现出令人鼓舞的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
