  In recent years, Internet users are reporting Adverse Drug Events (ADE) on
social media, blogs and health forums. Because of the large volume of reports,
pharmacovigilance is seeking to resort to NLP to monitor these outlets. We
propose for the first time the use of the SpanBERT architecture for the task of
ADE extraction: this new version of the popular BERT transformer showed
improved capabilities with multi-token text spans. We validate our hypothesis
with experiments on two datasets (SMM4H and CADEC) with different text
typologies (tweets and blog posts), finding that SpanBERT combined with a CRF
outperforms all the competitors on both of them.
