Papers with peer reviewing

4 papers
GrapAL: Connecting the Dots in Scientific Literature (P19-3)

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Challenge: Several software tools are available to help researchers perform tasks such as searching for papers, assessing applicants for a research position and keeping track of papers published on topics of interest.
Approach: They introduce a graph database of academic literature with an intuitive schema and query language . they open source the demo code to help other researchers develop applications that build on it .
Outcome: The proposed tool can be used to find experts on a given topic for peer review, find indirect connections between biomedical entities, and compute citation-based metrics.
Does My Rebuttal Matter? Insights from a Major NLP Conference (N19-1)

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Challenge: Peer review is a core element of the scientific process, but few studies have evaluated its properties empirically.
Approach: They propose to use peer review to assess the effectiveness of rebuttal phase in NLP conferences.
Outcome: The proposed task predicts after-rebuttal scores from initial reviews and author responses.
Uncertainty Aware Review Hallucination for Science Article Classification (2021.findings-acl)

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Challenge: Existing approaches to peer review support are limited in their use of available information and subjectivity.
Approach: They propose to use aleatory uncertainty and loss importance interpolations to model review representations at test time to provide a realistic evaluation framework.
Outcome: The proposed framework makes better use of the available information and is realistic with respect to the limitations set by the task 1 .
Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines (2024.lrec-main)

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Challenge: Existing citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft paper.
Approach: They propose a task of Recommending Missed Citations Identified by Reviewers to help improve citations of full papers.
Outcome: The proposed framework outperforms existing methods in all metrics and will motivate future research on this challenging task.

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