Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
This work addresses the challenge of adapting language models to specific domains without labeled data, offering incremental improvements over existing methods.
The paper tackles the problem of improving unsupervised domain adaptation for language models by proposing a novel adversarial masking strategy that focuses self-supervision on harder-to-reconstruct tokens, resulting in up to +1.64 F1 score improvements on six named entity recognition tasks.
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.