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We propose the task of automatically identifying papers used as baselines ina scientific article. We frame the problem as a binary classification taskwhere all the references in a paper are to be classified as either baselines ornon-baselines. This is a challenging problem due to the numerous ways in whicha baseline reference can appear in a paper. We develop a dataset of $2,075$papers from ACL anthology corpus with all their references manually annotatedas one of the two classes. We develop a multi-module attention-based neuralclassifier for the baseline classification task that outperforms fourstate-of-the-art citation role classification methods when applied to thebaseline classification task. We also present an analysis of the errors made bythe proposed classifier, eliciting the challenges that make baselineidentification a challenging problem.

Why Did You Not Compare With That? Identifying Papers for Use as Baselines