@inproceedings{verma-etal-2022-lack,
title = "The lack of theory is painful: Modeling Harshness in Peer Review Comments",
author = "Verma, Rajeev and
Roychoudhury, Rajarshi and
Ghosal, Tirthankar",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "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 = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.67",
pages = "925--935",
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 \url{https://github.com/Tirthankar-Ghosal/moderating-peer-review-harshness}. Our research is one step towards helping create constructive peer-review reports.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T The lack of theory is painful: Modeling Harshness in Peer Review Comments
%A Verma, Rajeev
%A Roychoudhury, Rajarshi
%A Ghosal, Tirthankar
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S 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)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F verma-etal-2022-lack
%X 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.
%U https://aclanthology.org/2022.aacl-main.67
%P 925-935
Markdown (Informal)
[The lack of theory is painful: Modeling Harshness in Peer Review Comments](https://aclanthology.org/2022.aacl-main.67) (Verma et al., AACL-IJCNLP 2022)
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.