HassanB4/aragenre-synthetic-training-data

AraGenre Synthetic Training Data

Synthetic Arabic text corpus generated by NAMAA Community in support of a submission to the AraGenre 2026 shared task (hierarchical Arabic genre classification, ArabicNLP 2026 / EMNLP 2026). The data was produced as a candidate training-augmentation resource for the task's low-resource setting.

Dataset Summary

2,600 DeepSeek-V3-generated Arabic texts, each labelled with a broad_genre and specific_genre pair from the AraGenre taxonomy, split across 3 subsets:

Subset	File	Count	Purpose
Part 1 — train-genre texts	synth_train_genres.json	1,400	matches the 7 specific genres seen in the AraGenre train taxonomy
Part 2 — dev-genre texts	synth_dev_genres.json	900	matches the 6 specific genres seen in the AraGenre dev taxonomy
Part 3 — hard borderline-case texts	synth_hard_cases.json	300	harder examples for 5 specific genres, intended to probe genre boundaries
Combined	synth_all.json	2,600	union of Parts 1–3

Supported Tasks

• text-classification: predict broad_genre and/or specific_genre for a short Arabic text, per the AraGenre hierarchical taxonomy.

Languages

Arabic (ar). Genre definitions used to prompt the generation model were in English, per the AraGenre task's definition-guided framework, but all released text fields are Arabic.

Dataset Structure

Data Instances

Example record from synth_all.json:

{
  "id": "synth_train_community_discussions_0044",
  "text": "أنا جالي فرصة أغير جنسيتي بس رفضت عشان حاسس إني مصري مش هقدر أكون حاجة تانية.",
  "broad_genre": "Interactive",
  "specific_genre": "Community Discussions",
  "source": "synthetic_deepseek"
}

Data Fields

Field	Type	Description
id	string	unique record identifier
text	string	Arabic text snippet
broad_genre	string	one of the 6 AraGenre broad categories (Informative, Interactive, Creative, Learning, Legal, Religious)
specific_genre	string	fine-grained genre subtype within the broad category
source	string	synthetic_deepseek

Genre Breakdown

Part 1 — train genres (synth_train_genres.json, 200 texts each): Personal Narratives (Creative), Cultural Commentary (Informative), Public Service Announcements (Informative), Community Discussions (Interactive), Instructional Guides (Learning), Administrative Regulations (Legal), Official Statements (Legal).

Part 2 — dev genres (synth_dev_genres.json, 150 texts each): Motivational Writing (Creative), Analytical Reports (Informative), Advice Columns (Interactive), Educational Explanations (Learning), Diplomatic Communication (Legal), Religious Commentary (Religious).

Part 3 — hard borderline-case texts (synth_hard_cases.json, 300 total): Analytical Reports (100), Advice Columns (50), Educational Explanations (50), Motivational Writing (50), Religious Commentary (50) — all dev-genre specific genres, generated to be harder / more boundary-ambiguous instances than Part 2.

Generation Method

• Model: DeepSeek-V3
• Parallelism: 12 parallel API workers
• Prompting: each generation was prompted with the target genre's official AraGenre English definition and asked to produce an Arabic text matching that genre's communicative function, register, and structure — not just its topic.

Intended Use

This dataset is intended for training-data augmentation when building Arabic genre classifiers. The AraGenre 2026 shared task rules permit participants to use LLMs to generate synthetic training data.

Authors

• Hassan Barmandah (NAMAA Community; Umm Al-Qura University)
• Israa Elhosiny (NAMAA Community)
• Yousra El-Ghawi (NAMAA Community)
• Omer Nacar (NAMAA Community)

License

Apache License 2.0.

Citation

If you use this work, please cite our system-description paper:

@inproceedings{barmandah-etal-2026-namaa,
  title = {NAMAA at AraGenre 2026: From Encoder Baselines to Self-Consistent LLM Ensembling for Hierarchical Arabic Genre Classification},
  author = {Barmandah, Hassan and Elhosiny, Israa and El-Ghawi, Yousra and Nacar, Omer},
  booktitle = {Proceedings of the 4th Arabic Natural Language Processing Conference (ArabicNLP 2026)},
  address = {Budapest, Hungary},
  publisher = {Association for Computational Linguistics},
  year = {2026},
}

Please also cite the AraGenre 2026 shared task overview paper:

@inproceedings{elhaj-etal-2026-aragenre,
  title = {AraGenre 2026: A Hierarchical Definition-Guided Arabic Genre Classification Shared Task},
  author = {El-Haj, Mo and Ezzini, Saad and Abudalfa, Shadi and Lamsiyah, Salima and Jarrar, Mustafa},
  booktitle = {Proceedings of the 4th Arabic Natural Language Processing Conference (ArabicNLP 2026)},
  address = {Budapest, Hungary},
  publisher = {Association for Computational Linguistics},
  year = {2026},
}