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Dataset Card for AraStance

Dataset Summary

The dataset is a collection of news titles in arabic along with paraphrased and corrupted titles. The stance prediction version is a 3-class classification task. Data contains three columns: s1, s2, stance.

Languages

Arabic

Dataset Structure

Data Instances

An example of 'train' looks as follows:

{
  'id': '0', 
  's1': 'هجوم صاروخي يستهدف مطار في طرابلس ويجبر ليبيا على تغيير مسار الرحلات الجوية', 
  's2': 'هدوء الاشتباكات فى طرابلس', 
  'stance': 0
}

Data Fields

id: a 'string' feature.
s1: a 'string' expressing a claim/topic.
s2: a 'string' to be classified for its stance to the source.
stance: a class label representing the stance the article expresses towards the claim. Full tagset with indices:

                0: "disagree",
                1: "agree",
                2: "other",

Data Splits

name	instances
train	2652
validation	755
test	379

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

The dataset is curated by the paper's authors

Licensing Information

The authors distribute this data under the Apache License, Version 2.0

Citation Information

@inproceedings{,
    title = "Stance Prediction and Claim Verification: An {A}rabic Perspective", 
    author = "Khouja, Jude",
    booktitle = "Proceedings of the Third Workshop on Fact Extraction and {VER}ification ({FEVER})",
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
}

Contributions

Thanks to mkonxd for adding this dataset.

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