Recent work has shown the importance of adaptation of broad-coverage contextualised embedding models on the domain of the target task of interest. Current self-supervised adaptation methods are simplistic, as the training signal comes from a small percentage of \emph{randomly} masked-out tokens. In this paper, we show that careful masking strategies can bridge the knowledge gap of masked language models (MLMs) about the domains more effectively by allocating self-supervision where it is needed. Furthermore, we propose an effective training strategy by adversarially masking out those tokens which are harder to reconstruct by the underlying MLM. The adversarial objective leads to a challenging combinatorial optimisation problem over \emph{subsets} of tokens, which we tackle efficiently through relaxation to a variational lowerbound and dynamic programming. On six unsupervised domain adaptation tasks involving named entity recognition, our method strongly outperforms the random masking strategy and achieves up to +1.64 F1 score improvements.

本文探讨了如何利用适当的遮罩策略来提高基于掩蔽的语言模型在领域自适应任务上的性能，并提出了一种有效的训练策略，即通过对更难以恢复的标记进行对抗性遮蔽，以使模型更好地适应目标任务。通过六个无监督领域适应任务，该方法在命名实体识别方面显著优于随机遮蔽策略，提高了1.64个F1分数。

带对抗训练的语言模型有效无监督领域自适应