  Identifying the intent of a citation in scientific papers (e.g., background
information, use of methods, comparing results) is critical for machine reading
of individual publications and automated analysis of the scientific literature.
We propose structural scaffolds, a multitask model to incorporate structural
information of scientific papers into citations for effective classification of
citation intents. Our model achieves a new state-of-the-art on an existing ACL
anthology dataset (ACL-ARC) with a 13.3% absolute increase in F1 score, without
relying on external linguistic resources or hand-engineered features as done in
existing methods. In addition, we introduce a new dataset of citation intents
(SciCite) which is more than five times larger and covers multiple scientific
domains compared with existing datasets. Our code and data are available at:
https://github.com/allenai/scicite.
