Abstract
Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious. We argue that a part of the problem is that the reviewers and area chairs face a poorly defined task forcing apples-to-oranges comparisons. There are several potential ways forward, but the key difficulty is creating the incentives and mechanisms for their consistent implementation in the NLP community.
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CITATION STYLE
Rogers, A., & Augenstein, I. (2020). What can we do to improve peer review in NLP? In Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020 (pp. 1256–1262). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.findings-emnlp.112
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