mlsanityData to 7 Oct 2026

ReAct: A Review Comment Dataset for Actionability (and more)

arXiv 2210.00443 ↗First posted 2 Oct 2022cs.CL cs.LGcited by 5

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Abstract (arXiv)

Review comments play an important role in the evolution of documents. For a large document, the number of review comments may become large, making it difficult for the authors to quickly grasp what the comments are about. It is important to identify the nature of the comments to identify which comments require some action on the part of document authors, along with identifying the types of these comments. In this paper, we introduce an annotated review comment dataset ReAct. The review comments are sourced from OpenReview site. We crowd-source annotations for these reviews for actionability and type of comments. We analyze the properties of the dataset and validate the quality of annotations. We release the dataset (https://github.com/gtmdotme/ReAct) to the research community as a major contribution. We also benchmark our data with standard baselines for classification tasks and analyze their performance.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 16 references that are in the map. First posted on arXiv 2 Oct 2022.

Builds on

6 of 16 references.

References · 6

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  3. 2018Universal Sentence Encodercited by 485
  4. 2018A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applicationscited by 135
  5. 2020An Open Review of OpenReview: A Critical Analysis of the Machine Learning Conference Review Processcited by 23
  6. 2015Identifying Actionable Messages on Social Mediacited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 3; 2026: 1.

5 papers.