We study acquisition functions for active learning (AL) for text
classification. The Expected Loss Reduction (ELR) method focuses on a Bayesian
estimate of the reduction in classification error, recently updated with Mean
Objective Cost of Uncertainty (MOCU). We convert the ELR framework to estimate
the increase in (strictly proper) scores like log probability or negative mean
square error, which we call Bayesian Estimate of Mean Proper Scores (BEMPS). We
also prove convergence results borrowing techniques used with MOCU. In order to
allow better experimentation with the new acquisition functions, we develop a
complementary batch AL algorithm, which encourages diversity in the vector of
expected changes in scores for unlabelled data. To allow high performance text
classifiers, we combine ensembling and dynamic validation set construction on
pretrained language models. Extensive experimental evaluation then explores how
these different acquisition functions perform. The results show that the use of
mean square error and log probability with BEMPS yields robust acquisition
functions, which consistently outperform the others tested.

本研究探讨文本分类的主动学习领域中的不同收集函数，并开发了一种用于未标记数据的预期分数变化向量的多样性的批量主动学习算法，最后结合集成式学习和动态验证集构建证明 BEMPS 的 mean square error 和 log probability 收集函数的优越表现。