Active learning (AL) aims to reduce labeling costs by querying the examples
most beneficial for model learning. While the effectiveness of AL for
fine-tuning transformer-based pre-trained language models (PLMs) has been
demonstrated, it is less clear to what extent the AL gains obtained with one
model transfer to others. We consider the problem of transferability of
actively acquired datasets in text classification and investigate whether AL
gains persist when a dataset built using AL coupled with a specific PLM is used
to train a different PLM. We link the AL dataset transferability to the
similarity of instances queried by the different PLMs and show that AL methods
with similar acquisition sequences produce highly transferable datasets
regardless of the models used. Additionally, we show that the similarity of
acquisition sequences is influenced more by the choice of the AL method than
the choice of the model.

本文研究主题为探究主动学习中数据集可迁移性的影响因素，结果表明，使用相似的问询顺序能够获得高度可迁移的数据集，掌握掌握合适的主动学习方法比选择合适的模型更加重要。