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Domain adaptation of Pretrained Language Models (PTLMs) is typically achievedby unsupervised pretraining on target-domain text. While successful, thisapproach is expensive in terms of hardware, runtime and CO_2 emissions. Here,we propose a cheaper alternative: We train Word2Vec on target-domain text andalign the resulting word vectors with the wordpiece vectors of a general-domainPTLM. We evaluate on eight biomedical Named Entity Recognition (NER) tasks andcompare against the recently proposed BioBERT model. We cover over 60% of theBioBERT-BERT F1 delta, at 5% of BioBERT's CO_2 footprint and 2% of its cloudcompute cost. We also show how to quickly adapt an existing general-domainQuestion Answering (QA) model to an emerging domain: the Covid-19 pandemic.

Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA