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Dataset Card for peer_read

Dataset Summary

PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.

Supported Tasks and Leaderboards

[More Information Needed]



Dataset Structure

Data Instances

[More Information Needed]

Data Fields


  • name: string Filename in the dataset
  • metadata: dict Paper metadata
    • source: string Paper source
    • authors: list<string> List of paper authors
    • title: string Paper title
    • sections: list<dict> List of section heading and corresponding description
      • heading: string Section heading
      • text: string Section description
    • references: string List of references
      • title: string Title of reference paper
      • author: list<string> List of reference paper authors
      • venue: string Reference venue
      • citeRegEx: string Reference citeRegEx
      • shortCiteRegEx: string Reference shortCiteRegEx
      • year: int Reference publish year
    • referenceMentions: list<string> List of reference mentions
      • referenceID: int Reference mention ID
      • context: string Reference mention context
      • startOffset: int Reference startOffset
      • endOffset: int Reference endOffset
    • year: int Paper publish year
    • abstractText: string Paper abstract
    • creator: string Paper creator


  • id: int Review ID
  • conference: string Conference name
  • comments: string Review comments
  • subjects: string Review subjects
  • version: string Review version
  • date_of_submission: string Submission date
  • title: string Paper title
  • authors: list<string> List of paper authors
  • accepted: bool Paper accepted flag
  • abstract: string Paper abstract
  • histories: list<string> Paper details with link
  • reviews: dict Paper reviews
    • date: string Date of review
    • title: string Paper title
    • other_keys: string Reviewer other details
    • originality: string Originality score
    • comments: string Reviewer comments
    • is_meta_review: bool Review type flag
    • recommendation: string Reviewer recommendation
    • replicability: string Replicability score
    • presentation_format: string Presentation type
    • clarity: string Clarity score
    • meaningful_comparison: string Meaningful comparison score
    • substance: string Substance score
    • reviewer_confidence: string Reviewer confidence score
    • soundness_correctness: string Soundness correctness score
    • appropriateness: string Appropriateness score
    • impact: string Impact score

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]


Annotation process

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Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz

Licensing Information

[More Information Needed]

Citation Information

@inproceedings{kang18naacl, title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications}, author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz}, booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)}, address = {New Orleans, USA}, month = {June}, url = {}, year = {2018} }


Thanks to @vinaykudari for adding this dataset.

Update on GitHub
Papers with Code