Active learning (AL) uses a data selection algorithm to select useful
training samples to minimize annotation cost. This is now an essential tool for
building low-resource syntactic analyzers such as part-of-speech (POS) taggers.
Existing AL heuristics are generally designed on the principle of selecting
uncertain yet representative training instances, where annotating these
instances may reduce a large number of errors. However, in an empirical study
across six typologically diverse languages (German, Swedish, Galician, North
Sami, Persian, and Ukrainian), we found the surprising result that even in an
oracle scenario where we know the true uncertainty of predictions, these
current heuristics are far from optimal. Based on this analysis, we pose the
problem of AL as selecting instances which maximally reduce the confusion
between particular pairs of output tags. Extensive experimentation on the
aforementioned languages shows that our proposed AL strategy outperforms other
AL strategies by a significant margin. We also present auxiliary results
demonstrating the importance of proper calibration of models, which we ensure
through cross-view training, and analysis demonstrating how our proposed
strategy selects examples that more closely follow the oracle data
distribution.

这篇论文提出了一种主动学习策略，其中数据筛选算法用于选择有用的训练样本以最小化注释成本，在六种语言中进行了广泛的实验，证明了该策略优于其他主动学习策略，并提出了在特定的输出标签对之间最大程度减少混淆的 AL 问题。