What Can We Do to Improve Peer Review in NLP?

Anna Rogers, Isabelle Augenstein


Abstract
Peer review is our best tool for judging the quality of conference submissions, but it is becoming increasingly spurious. We argue that a part of the problem is that the reviewers and area chairs face a poorly defined task forcing apples-to-oranges comparisons. There are several potential ways forward, but the key difficulty is creating the incentives and mechanisms for their consistent implementation in the NLP community.
Anthology ID:
2020.findings-emnlp.112
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1256–1262
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.112
DOI:
10.18653/v1/2020.findings-emnlp.112
Bibkey:
Cite (ACL):
Anna Rogers and Isabelle Augenstein. 2020. What Can We Do to Improve Peer Review in NLP?. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1256–1262, Online. Association for Computational Linguistics.
Cite (Informal):
What Can We Do to Improve Peer Review in NLP? (Rogers & Augenstein, Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.112.pdf