  Biomedical entity linking is the task of identifying mentions of biomedical
concepts in text documents and mapping them to canonical entities in a target
thesaurus. Recent advancements in entity linking using BERT-based models follow
a retrieve and rerank paradigm, where the candidate entities are first selected
using a retriever model, and then the retrieved candidates are ranked by a
reranker model. While this paradigm produces state-of-the-art results, they are
slow both at training and test time as they can process only one mention at a
time. To mitigate these issues, we propose a BERT-based dual encoder model that
resolves multiple mentions in a document in one shot. We show that our proposed
model is multiple times faster than existing BERT-based models while being
competitive in accuracy for biomedical entity linking. Additionally, we modify
our dual encoder model for end-to-end biomedical entity linking that performs
both mention span detection and entity disambiguation and out-performs two
recently proposed models.
