  In the era of digital healthcare, the huge volumes of textual information
generated every day in hospitals constitute an essential but underused asset
that could be exploited with task-specific, fine-tuned biomedical language
representation models, improving patient care and management. For such
specialized domains, previous research has shown that fine-tuning models
stemming from broad-coverage checkpoints can largely benefit additional
training rounds over large-scale in-domain resources. However, these resources
are often unreachable for less-resourced languages like Italian, preventing
local medical institutions to employ in-domain adaptation. In order to reduce
this gap, our work investigates two accessible approaches to derive biomedical
language models in languages other than English, taking Italian as a concrete
use-case: one based on neural machine translation of English resources,
favoring quantity over quality; the other based on a high-grade, narrow-scoped
corpus natively written in Italian, thus preferring quality over quantity. Our
study shows that data quantity is a harder constraint than data quality for
biomedical adaptation, but the concatenation of high-quality data can improve
model performance even when dealing with relatively size-limited corpora. The
models published from our investigations have the potential to unlock important
research opportunities for Italian hospitals and academia. Finally, the set of
lessons learned from the study constitutes valuable insights towards a solution
to build biomedical language models that are generalizable to other
less-resourced languages and different domain settings.
