  Extracting relationships and interactions between different biological
entities is still an extremely challenging problem but has not received much
attention as much as extraction in other generic domains. In addition to the
lack of annotated data, low benchmarking is still a major reason for slow
progress. In order to fill this gap, we compare multiple existing entity and
relation extraction models over a recently introduced public dataset, BioRelEx
of sentences annotated with biological entities and relations. Our
straightforward benchmarking shows that span-based multi-task architectures
like DYGIE show 4.9% and 6% absolute improvements in entity tagging and
relation extraction respectively over the previous state-of-art and that
incorporating domain-specific information like embeddings pre-trained over
related domains boosts performance.
