annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
licenses:
- cc-by-nc-sa-4-0
multilinguality:
- monolingual
- other-language-learner
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-GUG-grammaticality-judgements
task_categories:
- conditional-text-generation
task_ids:
- conditional-text-generation-other-grammatical-error-correction
paperswithcode_id: jfleg
pretty_name: JHU FLuency-Extended GUG corpus
Dataset Card for JFLEG
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: Github
- Repository: Github
- Paper: Napoles et al., 2020
- Leaderboard: Leaderboard
- Point of Contact: Courtney Napoles, Keisuke Sakaguchi
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
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
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
[More Information Needed]
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.