  The prevalence of ambiguous acronyms make scientific documents harder to
understand for humans and machines alike, presenting a need for models that can
automatically identify acronyms in text and disambiguate their meaning. We
introduce new methods for acronym identification and disambiguation: our
acronym identification model projects learned token embeddings onto tag
predictions, and our acronym disambiguation model finds training examples with
similar sentence embeddings as test examples. Both of our systems achieve
significant performance gains over previously suggested methods, and perform
competitively on the SDU@AAAI-21 shared task leaderboard. Our models were
trained in part on new distantly-supervised datasets for these tasks which we
call AuxAI and AuxAD. We also identified a duplication conflict issue in the
SciAD dataset, and formed a deduplicated version of SciAD that we call
SciAD-dedupe. We publicly released all three of these datasets, and hope that
they help the community make further strides in scientific document
understanding.
