  End-to-end relation extraction (E2ERE) is an important task in information
extraction, more so for biomedicine as scientific literature continues to grow
exponentially. E2ERE typically involves identifying entities (or named entity
recognition (NER)) and associated relations, while most RE tasks simply assume
that the entities are provided upfront and end up performing relation
classification. E2ERE is inherently more difficult than RE alone given the
potential snowball effect of errors from NER leading to more errors in RE. A
complex dataset in biomedical E2ERE is the ChemProt dataset (BioCreative VI,
2017) that identifies relations between chemical compounds and genes/proteins
in scientific literature. ChemProt is included in all recent biomedical natural
language processing benchmarks including BLUE, BLURB, and BigBio. However, its
treatment in these benchmarks and in other separate efforts is typically not
end-to-end, with few exceptions. In this effort, we employ a span-based
pipeline approach to produce a new state-of-the-art E2ERE performance on the
ChemProt dataset, resulting in $> 4\%$ improvement in F1-score over the prior
best effort. Our results indicate that a straightforward fine-grained
tokenization scheme helps span-based approaches excel in E2ERE, especially with
regards to handling complex named entities. Our error analysis also identifies
a few key failure modes in E2ERE for ChemProt.
