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EMNLP Findings2020

What Can We Do to Improve Peer Review in NLP?

Anna Rogers, Isabelle Augenstein

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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the full paper · 7 top-tier papers that cite it · related work

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Cited by top-tier papers7

  1. NLPeer: A Unified Resource for the Computational Study of Peer ReviewNils Dycke, Ilia Kuznetsov, Iryna GurevychACL 2023 · 17 citations
  2. SciCoQA: Quality Assurance for Scientific Paper-Code AlignmentTim Baumgärtner, Iryna GurevychACL 2026 · 5 citations
  3. UnWEIRDing Peer Review in Human-Computer InteractionHellina Hailu Nigatu, Farhana Shahid, Vishal Sharma, Abigail Oppong et al.CHI 2026 · 2 citations
  4. LazyReview: A Dataset for Uncovering Lazy Thinking in NLP Peer ReviewsSukannya Purkayastha, Zhuang Li, Anne Lauscher, Lizhen Qu et al.ACL 2025 · 1 citation
  5. The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for AuthorsAbdelrahman Sadallah, Tim Baumgärtner, Iryna Gurevych, Ted BriscoeEMNLP 2025

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  2. Evaluating the Impact of Reviewer Guideline Design on LLM-Based Automated Peer ReviewHaowen Li, Yoichi Ishibashi, Masafumi OyamadaACL Findings 2026
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