README.md

Dataset Card for ANETAC

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

• Homepage: [info]
• Repository: [info]
• Paper: [info]
• Leaderboard: [info]
• Point of Contact: [info]

Dataset Summary

[More Information Needed]

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

[More Information Needed]

Data Fields

[More Information Needed]

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]

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

[More Information Needed]

Citation Information

@article{HADJAMEUR2017287,
title = "Arabic Machine Transliteration using an Attention-based Encoder-decoder Model",
journal = "Procedia Computer Science",
volume = "117",
pages = "287 - 297",
year = "2017",
note = "Arabic Computational Linguistics",
issn = "1877-0509",
doi = "https://doi.org/10.1016/j.procs.2017.10.120",
url = "http://www.sciencedirect.com/science/article/pii/S1877050917321774",
author = "Mohamed Seghir Hadj Ameur and Farid Meziane and Ahmed Guessoum",
keywords = "Natural Language Processing, Arabic Language, Arabic Transliteration, Deep Learning, Sequence-to-sequence Models, Encoder-decoder Architecture, Recurrent Neural Networks",
abstract = "Transliteration is the process of converting words from a given source language alphabet to a target language alphabet, in a way that best preserves the phonetic and orthographic aspects of the transliterated words. Even though an important effort has been made towards improving this process for many languages such as English, French and Chinese, little research work has been accomplished with regard to the Arabic language. In this work, an attention-based encoder-decoder system is proposed for the task of Machine Transliteration between the Arabic and English languages. Our experiments proved the efficiency of our proposal approach in comparison to some previous research developed in this area."
}

Contributions

Thanks to @github-username for adding this dataset.