Papers with review quality

8 papers
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2023.acl-long)

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Challenge: a program chair's job is to help reviewers and reviewers make better decisions . 80% of reviewers, reviewers voted to change the criteria for soundness and excitement .
Approach: a new process for matching papers to reviewers based on keywords was proposed . the authors have also brought back miniconf and RocketChat to allow for better virtual communication .
Outcome: a new process for matching papers to reviewers based on keywords allowed more fine-grained control over the paper-reviewer matches.
Findings of the Association for Computational Linguistics: ACL 2023 (2023.findings-acl)

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Challenge: a program chair's job is to help reviewers and reviewers make better decisions . 80% of reviewers, reviewers voted to change the criteria for soundness and excitement .
Approach: a new process for matching papers to reviewers based on keywords was proposed . the authors have also brought back miniconf and RocketChat to allow for better virtual communication .
Outcome: a new process for matching papers to reviewers based on keywords allowed more fine-grained control over the paper-reviewer matches.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (2023.acl-short)

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Challenge: a program chair's job is to help reviewers and reviewers make better decisions . 80% of reviewers, reviewers voted to change the criteria for soundness and excitement .
Approach: a new process for matching papers to reviewers based on keywords was proposed . the authors have also brought back miniconf and RocketChat to allow for better virtual communication .
Outcome: a new process for matching papers to reviewers based on keywords allowed more fine-grained control over the paper-reviewer matches.
Towards Reliable Paper Contributions Annotation in the ACL Rolling Review (2026.findings-acl)

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Challenge: Identifying the types of contributions an article makes can help readers grasp its significance.
Approach: They propose to use a typology to categorize articles by their contributions to improve review quality and fairness.
Outcome: The ACL Rolling Review (ARR) introduced a typology requiring authors to specify their contributions to improve review quality and fairness.
The Double Bind: Revisiting Preprinting and Peer Review Two Years After the Removal of the ACL Anonymity Period (2026.findings-acl)

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Challenge: ACL removed the anonymity period for conference submissions in February 2024 .
Approach: They track preprinting trends for 47k publications and analyze 1.9k peer reviews . they suggest improving visibility and investing in diversity initiatives .
Outcome: The proposed anonymity period was removed in 2024, but it was ineffective for underrepresented researchers . the authors suggest addressing D&I issues rather than implementing anonymity policies.
CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells (2025.naacl-long)

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Challenge: Current review comment evaluation metrics rely on comparisons with a human-written reference for a given code change (also called a diff).
Approach: They propose to use a reference-free metric to measure review quality like conciseness, comprehensiveness, and relevance to compare the quality of code changes with human-written references.
Outcome: The proposed metric can produce fine-grained scores that have the greatest alignment with human judgment and are more sensitive than reference-based metrics.
Program Chairs’ Report on Peer Review at ACL 2023 (2023.acl-long)

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Challenge: ACL'23 makes its peer review report public and an official part of the conference proceedings.
Approach: They present an analysis of the factors affecting peer review and identify the most problematic issues that the authors complained about.
Outcome: The authors identified the most problematic issues and provided suggestions for the future chairs.
PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality (2026.findings-acl)

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Challenge: Increasing use of large language models (LLMs) in academic review has raised concerns about quality and fairness.
Approach: They propose a framework to improve the quality of LLM-generated reviews by using retrieval-augmented generation.
Outcome: The proposed framework improves the human-level quality of LLM-generated reviews by adopting prompt engineering and retrieval-augmented generation.

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