  Preventable adverse drug reactions as a result of medical errors present a
growing concern in modern medicine. As drug-drug interactions (DDIs) may cause
adverse reactions, being able to extracting DDIs from drug labels into
machine-readable form is an important effort in effectively deploying drug
safety information. The DDI track of TAC 2018 introduces two large
hand-annotated test sets for the task of extracting DDIs from structured
product labels with linkage to standard terminologies. Herein, we describe our
approach to tackling tasks one and two of the DDI track, which corresponds to
named entity recognition (NER) and sentence-level relation extraction
respectively. Namely, our approach resembles a multi-task learning framework
designed to jointly model various sub-tasks including NER and interaction type
and outcome prediction. On NER, our system ranked second (among eight teams) at
33.00% and 38.25% F1 on Test Sets 1 and 2 respectively. On relation extraction,
our system ranked second (among four teams) at 21.59% and 23.55% on Test Sets 1
and 2 respectively.
