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Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
[article]
2021
arXiv
pre-print
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this paper, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language
arXiv:2007.15779v5
fatcat:emddce6qdzgmdmsboyrza27564