Active Learning (AL) is a method to iteratively select data for annotation from a pool of unlabeled data, aiming to achieve better model performance than random selection. Previous AL approaches in Natural Language Processing (NLP) have been limited to either task-specific models that are trained from scratch at each iteration using only the labeled data at hand or using off-the-shelf pretrained language models (LMs) that are not adapted effectively to the downstream task. In this paper, we address these limitations by introducing BALM; Bayesian Active Learning with pretrained language Models. We first propose to adapt the pretrained LM to the downstream task by continuing training with all the available unlabeled data and then use it for AL. We also suggest a simple yet effective fine-tuning method to ensure that the adapted LM is properly trained in both low and high resource scenarios during AL. We finally apply Monte Carlo dropout to the downstream model to obtain well-calibrated confidence scores for data selection with uncertainty sampling. Our experiments in five standard natural language understanding tasks demonstrate that BALM provides substantial data efficiency improvements compared to various combinations of acquisition functions, models and fine-tuning methods proposed in recent AL literature.

本文讨论使用预训练语言模型在自然语言处理中进行主动学习时存在的问题，并提出了使用先前所有的未标注数据来适应目标任务的解决方法，同时也提出了一种简单有效的微调方法来保证适应的语言模型在低资源和高资源情况下都能得到适当的训练，实验结果表明，相比标准的微调方法，我们的方法提供了显著的数据效率改进，这表明一个不良的训练策略可能对主动学习产生灾难性的影响。

关于有效地为主动学习调整预训练语言模型的重要性