@inproceedings{rogers-augenstein-2020-improve,
    title = "What Can We Do to Improve Peer Review in {NLP}?",
    author = "Rogers, Anna  and
      Augenstein, Isabelle",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.findings-emnlp.112/",
    doi = "10.18653/v1/2020.findings-emnlp.112",
    pages = "1256--1262",
    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."
}