Inexpensive Domain Adaptation of Pretrained Language Models: A Case Study on Biomedical Named Entity Recognition
share
Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by pretraining on in-domain text. While successful, this approach is expensive in terms of hardware, runtime and CO_2 emissions. Here, we propose a cheaper alternative: We train Word2Vec on in-domain text and align the resulting word vectors with the input space of a general-domain PTLM (here: BERT). We evaluate on eight biomedical Named Entity Recognition (NER) tasks and compare against the recently proposed BioBERT model (Lee et al., 2020). We cover over 50 the BioBERT-BERT F1 delta, at 5 cloud compute cost.
READ FULL TEXT
