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What Can Natural Language Processing Do for Peer Review?

What Can Natural Language Processing Do for Peer Review?

10 May 2024
Ilia Kuznetsov
Osama Mohammed Afzal
Koen Dercksen
Nils Dycke
Alexander Goldberg
Tom Hope
Dirk Hovy
Jonathan K. Kummerfeld
Anne Lauscher
Kevin Leyton-Brown
Sheng Lu
Mausam
Margot Mieskes
Aurélie Névéol
Danish Pruthi
Lizhen Qu
Roy Schwartz
Noah A. Smith
Thamar Solorio
Jingyan Wang
Xiaodan Zhu
Anna Rogers
Nihar B. Shah
Iryna Gurevych
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Papers citing "What Can Natural Language Processing Do for Peer Review?"

12 / 12 papers shown
Title
Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards
Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards
Jaeho Kim
Yunseok Lee
Seulki Lee
26
0
0
08 May 2025
Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review
Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review
Andrii Zahorodnii
Jasper J.F. van den Bosch
Ian Charest
Christopher Summerfield
Ila R. Fiete
68
0
0
22 Jan 2025
Benchmarking LLMs' Judgments with No Gold Standard
Benchmarking LLMs' Judgments with No Gold Standard
Shengwei Xu
Yuxuan Lu
Grant Schoenebeck
Yuqing Kong
29
1
0
11 Nov 2024
Computational Politeness in Natural Language Processing: A Survey
Computational Politeness in Natural Language Processing: A Survey
Priyanshu Priya
Mauajama Firdaus
Asif Ekbal
37
10
0
28 Jun 2024
LLMs as Meta-Reviewers' Assistants: A Case Study
LLMs as Meta-Reviewers' Assistants: A Case Study
Shubhra (Santu) Karmaker
Sanjeev Kumar Sinha
Naman Bansal
Alex Knipper
Souvik Sarkar
...
Matthew Freestone
Matthew C. Williams
Matthew C. Williams Jr.
Dongji Feng
Santu Karmaker
35
4
0
23 Feb 2024
Summarizing Multiple Documents with Conversational Structure for
  Meta-Review Generation
Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation
Miao Li
Eduard H. Hovy
Jey Han Lau
34
6
0
02 May 2023
Towards Automated Document Revision: Grammatical Error Correction,
  Fluency Edits, and Beyond
Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
Masato Mita
Keisuke Sakaguchi
Masato Hagiwara
Tomoya Mizumoto
Jun Suzuki
Kentaro Inui
33
13
0
23 May 2022
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
Neha Nayak Kennard
Timothy J. O'Gorman
Rajarshi Das
Akshay Sharma
Chhandak Bagchi
Matthew Clinton
Pranay Kumar Yelugam
Hamed Zamani
Andrew McCallum
23
26
0
16 Oct 2021
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation
Chenhui Shen
Liying Cheng
Ran Zhou
Lidong Bing
Yang You
Luo Si
34
33
0
14 Oct 2021
Just What do You Think You're Doing, Dave?' A Checklist for Responsible
  Data Use in NLP
Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP
Anna Rogers
Timothy Baldwin
Kobi Leins
102
64
0
14 Sep 2021
The Diversity-Innovation Paradox in Science
The Diversity-Innovation Paradox in Science
Bas Hofstra
V. V. Kulkarni
Sebastian Munoz-Najar Galvez
Bryan He
Dan Jurafsky
Daniel A. McFarland
17
661
0
04 Sep 2019
Fair prediction with disparate impact: A study of bias in recidivism
  prediction instruments
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
185
2,079
0
24 Oct 2016
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