Dataset Card for JFLEG

Dataset Summary

JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. It is a gold standard benchmark for developing and evaluating GEC systems with respect to fluency (extent to which a text is native-sounding) as well as grammaticality. For each source document, there are four human-written corrections.

Supported Tasks and Leaderboards

Grammatical error correction.

Languages

English (native as well as L2 writers)

Dataset Structure

Data Instances

Each instance contains a source sentence and four corrections. For example:

{
  'sentence': "They are moved by solar energy ."
  'corrections': [
    "They are moving by solar energy .",
    "They are moved by solar energy .",
    "They are moved by solar energy .",
    "They are propelled by solar energy ." 
  ]
}

Data Fields

• sentence: original sentence written by an English learner
• corrections: corrected versions by human annotators. The order of the annotations are consistent (eg first sentence will always be written by annotator "ref0").

Data Splits

• This dataset contains 1511 examples in total and comprise a dev and test split.
• There are 754 and 747 source sentences for dev and test, respectively.
• Each sentence has 4 corresponding corrected versions.

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

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Annotations

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .

Citation Information

This benchmark was proposed by Napoles et al., 2020 .

@InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,
  author    = {Napoles, Courtney  and  Sakaguchi, Keisuke  and  Tetreault, Joel},
  title     = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {229--234},
  url       = {http://www.aclweb.org/anthology/E17-2037}
}

@InProceedings{heilman-EtAl:2014:P14-2,
  author    = {Heilman, Michael  and  Cahill, Aoife  and  Madnani, Nitin  and  Lopez, Melissa  and  Mulholland, Matthew  and  Tetreault, Joel},
  title     = {Predicting Grammaticality on an Ordinal Scale},
  booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {June},
  year      = {2014},
  address   = {Baltimore, Maryland},
  publisher = {Association for Computational Linguistics},
  pages     = {174--180},
  url       = {http://www.aclweb.org/anthology/P14-2029}
}

Contributions

Thanks to @j-chim for adding this dataset.