What Can We Do to Improve Peer Review in NLP?
- ,
- Isabelle Augenstein
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewPublication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOriginal language
EnglishPages from-to (Number of pages)
Pages 1256-1262 (7 pages)Publication milestones
- Published - 01/11/2020
Publication status
Published - 01/11/2020
Place of publication
OnlinePublisher
Association for Computational Linguistics, United StatesPublication IDs
- Scopus: 85101806625
Host publication title
Findings of EMNLPAbstract
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.
Publication metrics
PlumX
Citations
36