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Working within specific NLP subdomains presents significant challenges,primarily due to a persistent deficit of data. Stringent privacy concerns andlimited data accessibility often drive this shortage. Additionally, the medicaldomain demands high accuracy, where even marginal improvements in modelperformance can have profound impacts. In this study, we investigate thepotential of vocabulary transfer to enhance model performance in biomedical NLPtasks. Specifically, we focus on vocabulary extension, a technique thatinvolves expanding the target vocabulary to incorporate domain-specificbiomedical terms. Our findings demonstrate that vocabulary extension, leads tomeasurable improvements in both downstream model performance and inferencetime.

Vocabulary Transfer for Biomedical Texts: Add Tokens if You Can Not Add Data