
# Dataset Card for AraStance
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description
- **Repository:** [https://github.com/latynt/ans](https://github.com/latynt/ans)
- **Paper:** [https://arxiv.org/abs/2005.10410](https://arxiv.org/abs/2005.10410)
- **Point of Contact:** [Jude Khouja](jude@latynt.com)

### 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](https://github.com/mkonxd) for adding this dataset.
task_categories:text-classification task_ids:fact-checking annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:ar license:apache-2.0 stance-detection arxiv:2005.10410 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["ar"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["fact-checking"], "pretty_name": "ans-stance", "tags": ["stance-detection"]}
TAGS
#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #license-apache-2.0 #stance-detection #arxiv-2005.10410 #region-us 

Dataset Card for AraStance
==========================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Repository: URL
* Paper: URL
* Point of Contact: Jude Khouja


### 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:


### 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:


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


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


### Contributions


Thanks to mkonxd for adding this dataset.



### 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: ### 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: ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


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 ### Contributions


Thanks to mkonxd for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-fact-checking #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Arabic #license-apache-2.0 #stance-detection #arxiv-2005.10410 #region-us 
 ### 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: ### 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: ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


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 ### Contributions


Thanks to mkonxd for adding this dataset.