  To this date, the efficacy of the scientific publishing enterprise
fundamentally rests on the strength of the peer review process. The journal
editor or the conference chair primarily relies on the expert reviewers'
assessment, identify points of agreement and disagreement and try to reach a
consensus to make a fair and informed decision on whether to accept or reject a
paper. However, with the escalating number of submissions requiring review,
especially in top-tier Artificial Intelligence (AI) conferences, the
editor/chair, among many other works, invests a significant, sometimes
stressful effort to mitigate reviewer disagreements. Here in this work, we
introduce a novel task of automatically identifying contradictions among
reviewers on a given article. To this end, we introduce ContraSciView, a
comprehensive review-pair contradiction dataset on around 8.5k papers (with
around 28k review pairs containing nearly 50k review pair comments) from the
open review-based ICLR and NeurIPS conferences. We further propose a baseline
model that detects contradictory statements from the review pairs. To the best
of our knowledge, we make the first attempt to identify disagreements among
peer reviewers automatically. We make our dataset and code public for further
investigations.
