论文标题
大型会议的论文和审稿人
Matching Papers and Reviewers at Large Conferences
论文作者
论文摘要
CS的主要出版物场所进行的同行评审会议务必依赖每篇论文的高素质审阅者。由于这些会议的规模越来越大，它们的工作时间表以及最近明显不诚实的行为激增，现在没有其他选择以自动化的方式进行这种匹配。本文研究了最近在第35届AAAI人工智能会议（AAAI 2021）会议上部署的一种新型审阅者纸匹配方法，此后已通过ICML 2022，AAAAI 2022，AAAI 2022和IJCAI的其他会议（全部或部分）采用（全部或部分）。分数； （2）制定和解决优化问题，以找到良好的审阅者纸匹配； （3）一个两阶段的审查过程，将审查资源从可能被拒绝的文件转移到更接近决策界的文件。本文还根据对真实数据进行了广泛的事后分析来介绍对这些创新的评估，包括与AAAI先前（2020年）迭代中使用的匹配算法进行比较 - 并通过其他数值实验对此进行了补充。
Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper studies a novel reviewer-paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022, and IJCAI 2022. This approach has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer-paper scores; (2) formulating and solving an optimization problem to find good reviewer-paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data -- including a comparison with the matching algorithm used in AAAI's previous (2020) iteration -- and supplements this with additional numerical experimentation.
