A recent report of Littmann [Commun. ACM '21] outlines the existence and the
fatal impact of collusion rings in academic peer reviewing. We introduce and
analyze the problem Cycle-Free Reviewing that aims at finding a review
assignment without the following kind of collusion ring: A sequence of
reviewers each reviewing a paper authored by the next reviewer in the sequence
(with the last reviewer reviewing a paper of the first), thus creating a review
cycle where each reviewer gives favorable reviews. As a result, all papers in
that cycle have a high chance of acceptance independent of their respective
scientific merit.
We observe that review assignments computed using a standard Linear
Programming approach typically admit many short review cycles. On the negative
side, we show that Cycle-Free Reviewing is NP-hard in various restricted cases
(i.e., when every author is qualified to review all papers and one wants to
prevent that authors review each other's or their own papers or when every
author has only one paper and is only qualified to review few papers). On the
positive side, among others, we show that, in some realistic settings, an
assignment without any review cycles of small length always exists. This result
also gives rise to an efficient heuristic for computing (weighted) cycle-free
review assignments, which we show to be of excellent quality in practice.

本文旨在解决学术同行评审中的合谋环问题，引入 Cycle-Free Reviewing 概念，分析其难点，提出并验证线性规划方案的短评检测存在问题，最后提出了一种高效的计算赋权的无环评审分配的启发式方法，该方法在实践中具有良好的质量。

打击合谋圈很难但可行

Combating Collusion Rings is Hard but Possible

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'.

本文介绍了一个公开的科学领域数据集，提供了许多研究机会，该数据集集中在同行评审中使用的文本数据，此外，作者提出了两个基于 NLP 技术的新任务，包括简单的基准模型，第一个任务中，简单模型可以预测文章是否被接受，并且还可以在第二个任务中预测审稿方面的数值分数。