Papers by Rajeev Verma

2 papers
DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions (P19-1)

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Challenge: Existing peer review system is not straightforward and requires domain knowledge, expertise, and intelligence of human reviewers, which is somewhat elusive with the current state of AI.
Approach: They propose to use peer review texts to predict acceptance or rejection of a manuscript based on reviewer sentiment.
Outcome: The proposed deep neural architecture achieves significant performance improvement over baselines (29% error reduction) in a recently released dataset of peer reviews.
The lack of theory is painful: Modeling Harshness in Peer Review Comments (2022.aacl-main)

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Challenge: a new study shows that peer-review has a power imbalance, making it fraught for authors . authors argue that a little more effort to remain critical but be constructive would help foster a positive outcome .
Approach: They propose to use a dataset to show peer-review comments' harshness scores . they argue that this moderation could help authors to be more constructive .
Outcome: The proposed dataset shows that it can be used to make peer reviews less hurtful and more welcoming.

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