---
title: "Munazarat 1.0 — Khader 2024"
description: "Arabic NLP lacks open datasets compared with English and other major languages."
authors: ["Mohammad M. Khader", "AbdulGabbar Al-Sharafi", "Mohamad Hamza Al-Sioufy", "Wajdi Zaghouani", "Ali Al-Zawqari"]
year: 2024
venue: "Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024"
source: "https://aclanthology.org/2024.osact-1.3/"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/khader-2024-munazarat-1-0-corpus-arabic/"
---
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# Munazarat 1.0: A Corpus of Arabic Competitive Debates

**Authors:** Mohammad M. Khader, AbdulGabbar Al-Sharafi, Mohamad Hamza Al-Sioufy, [Wajdi Zaghouani](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/), Ali Al-Zawqari · **Year:** 2024 · **Venue:** Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024 · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.osact-1.3/) · [PDF](https://aclanthology.org/2024.osact-1.3.pdf) · [OpenAlex](https://openalex.org/W7131799435)

**Citations:** 1 citations (openalex-snapshot · 2026-10-14) · [4](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Arabic NLP lacks open datasets compared with English and other major languages. Data on spoken argumentation is especially scarce, even though competitive debating is common across the Arab world. The authors built Munazarat 1.0, a corpus of roughly 50 hours of transcribed speech from 73 debates held at tournaments recognized by QatarDebate Center between 2013 and 2023, covering both university and school levels. The recordings were already public on YouTube, and no further personal information about debaters was added. Speech was first converted to text with Fenek, Kanari AI's speech-to-text system. Three native Arabic speakers then checked the transcripts in a multi-stage review, and each debate comes with metadata. The language is mostly Modern Standard Arabic, and topics vary widely. According to the authors, it is the only corpus built specifically around competitive debating in Arabic, and no other collection of Arabic argumentative material is bigger. It is intended to support argument mining, speech recognition, Arabic NLP tool training, and studies of Arabic rhetoric. They plan to grow it to about 120 debates by the end of 2024.

## Key points
- Size and source: about 50 hours from 73 debates, collected from QatarDebate's public YouTube recordings spanning 2013–2023.
- Debate structure: a modified World Schools Debating Championship format with three speakers per side, 6–7 minute speeches in alternating order, then a 3-minute "Reply Speech" per team that may not add new arguments. An odd-numbered judge panel picks the winner and scores individual speakers.
- Pipeline: automatic transcription with Fenek (Kanari AI), then a human review in three stages. In the first stage, a reviewer listens to the video while correcting the machine output. Ten transcripts were reused from Khader (2020) and went only through stages two and three. The review guidelines are released with the data.
- Comparison with prior resources: QT30 has 30 BBC Question Time episodes, all political. Khader (2020) had only 12 Arabic debates. QCAW holds 195 Arabic and 195 English student essays, all written rather than spoken.
- Motivating statistics (March 2024): Papers With Code listed 54 Arabic text datasets versus 1446 for English. Hugging Face listed 446 Arabic text datasets out of 126,088.

## Interesting ideas
- Generate a cheap machine draft first, then have humans correct it against the original source in staged passes; this costs far less than transcribing from scratch while still catching tool errors. — For Mova (learn): silver UD trees agreed by witness parsers could get a similar staged review, where a later pass re-checks the tree against the raw sentence rather than only against other parsers' outputs.
- Material imported from earlier work entered the pipeline partway, skipping the first review stage, which makes each item's review history differ by origin. — For Mova (learn): store with every training example which checks it has passed, so that evaluation and repair-rule induction can weight or filter items by how thoroughly they were verified.
- Some genres have built-in discourse rules, such as the reply speech that is forbidden from adding new arguments, and these rules give free constraints to check analyses against. — For Mova (frames): speaker role and speech position could serve as priors for speech-act labeling, and a "summarizes but adds nothing new" segment can act as a test that extracted claims link back to earlier frames.
- Competitive formats produce outside judgments of persuasiveness (a winning team and per-speaker scores) without any extra annotation effort. — For Mova (answer): externally decided outcomes like these could become test items where a reader must explain which side argued better, with the verdict acting as a fixed reference answer.
- Fixed turn order with opposing sides offers a clean frame for tracking who argues against whom across a long multi-speaker text. — For Mova (world): model each speaker and team as entities whose positions on a motion are states updated turn by turn, so that refutations attach to the claims they target.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) · [Machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/)

## Cited by

Cited by

4 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 3 not yet.

- [Ara-Best-RQ: Multi Dialectal Arabic SSL](https://md.research.elamur.ai/free/en/papers/elleuch-2026-ara-best-rq-multi-dialectal/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{khader2024munazarat,
  title = {{Munazarat 1.0: A Corpus of Arabic Competitive Debates}},
  author = {Khader, Mohammad M. and Al-Sharafi, AbdulGabbar and Al-Sioufy, Mohamad Hamza and Zaghouani, Wajdi and Al-Zawqari, Ali},
  booktitle = {Proceedings of the 6th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT) with Shared Tasks on Arabic LLMs Hallucination and Dialect to MSA Machine Translation @ LREC-COLING 2024},
  year = {2024},
  doi = {10.63317/55ds55ofap7q},
  url = {https://doi.org/10.63317/55ds55ofap7q}
}
```

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