  We propose the task of automatically identifying papers used as baselines in
a scientific article. We frame the problem as a binary classification task
where all the references in a paper are to be classified as either baselines or
non-baselines. This is a challenging problem due to the numerous ways in which
a baseline reference can appear in a paper. We develop a dataset of $2,075$
papers from ACL anthology corpus with all their references manually annotated
as one of the two classes. We develop a multi-module attention-based neural
classifier for the baseline classification task that outperforms four
state-of-the-art citation role classification methods when applied to the
baseline classification task. We also present an analysis of the errors made by
the proposed classifier, eliciting the challenges that make baseline
identification a challenging problem.
