Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain. Although continued pre-training on a large domain-specific corpus is effective, it is costly to tune all the parameters on the domain. In this paper, we investigate whether we can adapt PLMs both effectively and efficiently by only tuning a few parameters. Specifically, we decouple the feed-forward networks (FFNs) of the Transformer architecture into two parts: the original pre-trained FFNs to maintain the old-domain knowledge and our novel domain-specific adapters to inject domain-specific knowledge in parallel. Then we adopt a mixture-of-adapters gate to fuse the knowledge from different domain adapters dynamically. Our proposed Mixture-of-Domain-Adapters (MixDA) employs a two-stage adapter-tuning strategy that leverages both unlabeled data and labeled data to help the domain adaptation: i) domain-specific adapter on unlabeled data; followed by ii) the task-specific adapter on labeled data. MixDA can be seamlessly plugged into the pretraining-finetuning paradigm and our experiments demonstrate that MixDA achieves superior performance on in-domain tasks (GLUE), out-of-domain tasks (ChemProt, RCT, IMDB, Amazon), and knowledge-intensive tasks (KILT).Further analyses demonstrate the reliability, scalability, and efficiency of our method.

Large pre-trained models such as BERT are known to improve different downstream NLP tasks, even when such a model is trained on a generic domain. Moreover, recent studies have shown that when large domain-specific corpora are available, continued pre-training on domain-specific data can further improve the performance of in-domain tasks. However, this practice requires significant domain-specific data and computational resources which may not always be available. In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data. We demonstrate that by explicitly incorporating multi-granularity information of unseen and domain-specific words via the adaptation of (word based) n-grams, the performance of a generic pretrained model can be greatly improved. Specifically, we introduce a Transformer-based Domain-aware N-gram Adaptor, T-DNA, to effectively learn and incorporate the semantic representation of different combinations of words in the new domain. Experimental results illustrate the effectiveness of T-DNA on eight low-resource downstream tasks from four domains. We show that T-DNA is able to achieve significant improvements compared to existing methods on most tasks using limited data with lower computational costs. Moreover, further analyses demonstrate the importance and effectiveness of both unseen words and the information of different granularities. Our code is available at https://github.com/shizhediao/T-DNA.