The lack of theory is painful: Modeling Harshness in Peer Review Comments

Rajeev Verma, Rajarshi Roychoudhury, Tirthankar Ghosal

Abstract
The peer-review system has primarily remained the central process of all science communications. However, research has shown that the process manifests a power-imbalance scenario where the reviewer enjoys a position where their comments can be overly critical and wilfully obtuse without being held accountable. This brings into question the sanctity of the peer-review process, turning it into a fraught and traumatic experience for authors. A little more effort to still remain critical but be constructive in the feedback would help foster a progressive outcome from the peer-review process. In this paper, we argue to intervene at the step where this power imbalance actually begins in the system. To this end, we develop the first dataset of peer-review comments with their real-valued harshness scores. We build our dataset by using the popular Best-Worst-Scaling mechanism. We show the utility of our dataset for text moderation in peer reviews to make review reports less hurtful and more welcoming. We release our dataset and associated codes in https://github.com/Tirthankar-Ghosal/moderating-peer-review-harshness. Our research is one step towards helping create constructive peer-review reports.
Anthology ID:
2022.aacl-main.67
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2022
Address:
Online only
Editors:
Yulan He, Heng Ji, Sujian Li, Yang Liu, Chua-Hui Chang
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
925–935
Language:
URL:
https://aclanthology.org/2022.aacl-main.67
DOI:
Bibkey:
Cite (ACL):
Rajeev Verma, Rajarshi Roychoudhury, and Tirthankar Ghosal. 2022. The lack of theory is painful: Modeling Harshness in Peer Review Comments. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 925–935, Online only. Association for Computational Linguistics.
Cite (Informal):
The lack of theory is painful: Modeling Harshness in Peer Review Comments (Verma et al., AACL-IJCNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2022.aacl-main.67.pdf
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