TY - GEN
T1 - The lack of theory is painful
T2 - 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, AACL-IJCNLP 2022
AU - Verma, Rajeev
AU - Roychowdhury, Rajarshi
AU - Ghosal, Tirthankar
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105027297264
U2 - 10.18653/v1/2022.aacl-main.67
DO - 10.18653/v1/2022.aacl-main.67
M3 - Conference contribution
AN - SCOPUS:105027297264
T3 - 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: Long Paper, AACL-IJCNLP 2022
SP - 925
EP - 935
BT - Long Papers
A2 - He, Yulan
A2 - Ji, Heng
A2 - Li, Sujian
A2 - Liu, Yang
A2 - Chang, Chua-Hui
PB - Association for Computational Linguistics (ACL)
Y2 - 20 November 2022 through 23 November 2022
ER -