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To this date, the efficacy of the scientific publishing enterprisefundamentally rests on the strength of the peer review process. The journaleditor or the conference chair primarily relies on the expert reviewers'assessment, identify points of agreement and disagreement and try to reach aconsensus to make a fair and informed decision on whether to accept or reject apaper. However, with the escalating number of submissions requiring review,especially in top-tier Artificial Intelligence (AI) conferences, theeditor/chair, among many other works, invests a significant, sometimesstressful effort to mitigate reviewer disagreements. Here in this work, weintroduce a novel task of automatically identifying contradictions amongreviewers on a given article. To this end, we introduce ContraSciView, acomprehensive review-pair contradiction dataset on around 8.5k papers (witharound 28k review pairs containing nearly 50k review pair comments) from theopen review-based ICLR and NeurIPS conferences. We further propose a baselinemodel that detects contradictory statements from the review pairs. To the bestof our knowledge, we make the first attempt to identify disagreements amongpeer reviewers automatically. We make our dataset and code public for furtherinvestigations.

When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews