Double-blind peer review is considered a pillar of academic research because
it is perceived to ensure a fair, unbiased, and fact-centered scientific
discussion. Yet, experienced researchers can often correctly guess from which
research group an anonymous submission originates, biasing the peer-review
process. In this work, we present a transformer-based, neural-network
architecture that only uses the text content and the author names in the
bibliography to atttribute an anonymous manuscript to an author. To train and
evaluate our method, we created the largest authorship-identification dataset
to date. It leverages all research papers publicly available on arXiv amounting
to over 2 million manuscripts. In arXiv-subsets with up to 2,000 different
authors, our method achieves an unprecedented authorship attribution accuracy,
where up to 95% of papers are attributed correctly. Thanks to our method, we
are not only able to predict the author of an anonymous work but we also
identify weaknesses of the double-blind review process by finding the key
aspects that make a paper attributable. We believe that this work gives
precious insights into how a submission can remain anonymous in order to
support an unbiased double-blind review process.

该研究提出了一种基于变形金刚模型的神经网络结构，仅使用文本内容和参考文献中的作者姓名来将匿名稿件归属于作者，并创建了迄今为止最大的作者身份识别数据集，并发现了双盲审稿流程中的一些弱点，为支持公正的双盲审稿流程提供了有价值的见解。