@inproceedings{caragea-etal-2019-myth,
title = "The Myth of Double-Blind Review Revisited: {ACL} vs. {EMNLP}",
author = "Caragea, Cornelia and
Uban, Ana and
Dinu, Liviu P.",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1236",
doi = "10.18653/v1/D19-1236",
pages = "2317--2327",
abstract = "The review and selection process for scientific paper publication is essential for the quality of scholarly publications in a scientific field. The double-blind review system, which enforces author anonymity during the review period, is widely used by prestigious conferences and journals to ensure the integrity of this process. Although the notion of anonymity in the double-blind review has been questioned before, the availability of full text paper collections brings new opportunities for exploring the question: Is the double-blind review process really double-blind? We study this question on the ACL and EMNLP paper collections and present an analysis on how well deep learning techniques can infer the authors of a paper. Specifically, we explore Convolutional Neural Networks trained on various aspects of a paper, e.g., content, style features, and references, to understand the extent to which we can infer the authors of a paper and what aspects contribute the most. Our results show that the authors of a paper can be inferred with accuracy as high as 87{\%} on ACL and 78{\%} on EMNLP for the top 100 most prolific authors.",
}
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<abstract>The review and selection process for scientific paper publication is essential for the quality of scholarly publications in a scientific field. The double-blind review system, which enforces author anonymity during the review period, is widely used by prestigious conferences and journals to ensure the integrity of this process. Although the notion of anonymity in the double-blind review has been questioned before, the availability of full text paper collections brings new opportunities for exploring the question: Is the double-blind review process really double-blind? We study this question on the ACL and EMNLP paper collections and present an analysis on how well deep learning techniques can infer the authors of a paper. Specifically, we explore Convolutional Neural Networks trained on various aspects of a paper, e.g., content, style features, and references, to understand the extent to which we can infer the authors of a paper and what aspects contribute the most. Our results show that the authors of a paper can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP for the top 100 most prolific authors.</abstract>
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%0 Conference Proceedings
%T The Myth of Double-Blind Review Revisited: ACL vs. EMNLP
%A Caragea, Cornelia
%A Uban, Ana
%A Dinu, Liviu P.
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F caragea-etal-2019-myth
%X The review and selection process for scientific paper publication is essential for the quality of scholarly publications in a scientific field. The double-blind review system, which enforces author anonymity during the review period, is widely used by prestigious conferences and journals to ensure the integrity of this process. Although the notion of anonymity in the double-blind review has been questioned before, the availability of full text paper collections brings new opportunities for exploring the question: Is the double-blind review process really double-blind? We study this question on the ACL and EMNLP paper collections and present an analysis on how well deep learning techniques can infer the authors of a paper. Specifically, we explore Convolutional Neural Networks trained on various aspects of a paper, e.g., content, style features, and references, to understand the extent to which we can infer the authors of a paper and what aspects contribute the most. Our results show that the authors of a paper can be inferred with accuracy as high as 87% on ACL and 78% on EMNLP for the top 100 most prolific authors.
%R 10.18653/v1/D19-1236
%U https://aclanthology.org/D19-1236
%U https://doi.org/10.18653/v1/D19-1236
%P 2317-2327
Markdown (Informal)
[The Myth of Double-Blind Review Revisited: ACL vs. EMNLP](https://aclanthology.org/D19-1236) (Caragea et al., EMNLP-IJCNLP 2019)
ACL
- Cornelia Caragea, Ana Uban, and Liviu P. Dinu. 2019. The Myth of Double-Blind Review Revisited: ACL vs. EMNLP. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2317–2327, Hong Kong, China. Association for Computational Linguistics.