Continual Domain-Tuning for Pretrained Language Models

Pre-trained language models (LM) such as BERT, DistilBERT, and RoBERTa can be tuned for different domains (domain-tuning) by continuing the pre-training phase on a new target domain corpus. This simple domain tuning (SDT) technique has been widely used to create domain-tuned models such as BioBERT, SciBERT and ClinicalBERT. However, during the pretraining phase on the target domain, the LM models may catastrophically forget the patterns learned from their source domain. In this work, we study the effects of catastrophic forgetting on domain-tuned LM models and investigate methods that mitigate its negative effects. We propose continual learning (CL) based alternatives for SDT, that aim to reduce catastrophic forgetting. We show that these methods may increase the performance of LM models on downstream target domain tasks. Additionally, we also show that constraining the LM model from forgetting the source domain leads to downstream task models that are more robust to domain shifts. We analyze the computational cost of using our proposed CL methods and provide recommendations for computationally lightweight and effective CL domain-tuning procedures.