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A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

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arxiv 1804.09635 v1 pith:HQKYQ63L submitted 2018-04-25 cs.CL cs.AI

Dongyeop Kang , Waleed Ammar , Bhavana Dalvi , Madeleine van Zuylen , Sebastian Kohlmeier , Eduard Hovy , Roy Schwartz This is my paper
classification cs.CLcs.AI
keywords datasetpeerreviewsbaselinemodelssimpleaspectscollection
verification ladder T0 review T1 audit T2 compute T3 formal
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Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead v1) providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. The dataset also includes 10.7K textual peer reviews written by experts for a subset of the papers. We describe the data collection process and report interesting observed phenomena in the peer reviews. We also propose two novel NLP tasks based on this dataset and provide simple baseline models. In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline. In the second task, we predict the numerical scores of review aspects and show that simple models can outperform the mean baseline for aspects with high variance such as 'originality' and 'impact'.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. How Far Are AI Scientists from Changing the World?

    cs.AI 2025-07 conditional novelty 4.0 of 10

    This survey proposes a four-level capability framework for AI Scientist systems and, using an AI reviewer, finds that current systems produce papers rated well below normal scientific standards.

  2. OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models

    cs.CY 2025-05 conditional novelty 4.0 of 10

    The paper advocates protecting and leveraging OpenReview's peer review corpus as a community asset for LLM-based review assistance, benchmarks, and alignment.

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