---
title: "ConfliBERT-Arabic — paper by Alsarra et al. (2023)"
description: "The authors build ConfliBERT-Arabic, an Arabic counterpart to the English ConfliBERT. 44 references, cited by 2 papers here."
authors: ["Sultan Alsarra", "Luay Abdeljaber", "Wooseong Yang", "Niamat Zawad", "Latifur Khan", "Patrick Brandt", "Javier Osorio", "Vito D'Orazio"]
year: 2023
venue: "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing"
source: "https://aclanthology.org/2023.ranlp-1.11/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/alsarra-2023-conflibert-arabic-pre-trained-arabic/"
---
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# ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence

**Authors:** Sultan Alsarra, Luay Abdeljaber, Wooseong Yang, Niamat Zawad, Latifur Khan, Patrick Brandt, [Javier Osorio](https://md.research.elamur.ai/free/en/authors/javier-osorio/) and Vito D'Orazio · **Year:** 2023 · **Venue:** [Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/ranlp/) ([RANLP 2023](https://md.research.elamur.ai/free/en/venues/ranlp/2023/)) · **Publisher:** [INCOMA Ltd.](https://md.research.elamur.ai/free/en/publishers/incoma-ltd/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.ranlp-1.11/) · [PDF](https://aclanthology.org/2023.ranlp-1.11.pdf) · [OpenAlex](https://openalex.org/W4388666050)

**Citations:** 6 citations (OpenAlex, 2026-10-14) · 2 in our library, of the 149,439 papers whose bibliographies we read

## Summary
General-purpose and Arabic-wide language models are not tuned to news about Middle Eastern politics, conflict and violence. Arabic also makes text analysis hard: it has no capitalization, it omits short vowels, and its affix-heavy morphology produces sparse data. The authors build ConfliBERT-Arabic, an Arabic counterpart to the English ConfliBERT. They assembled an 11.5GB pre-training corpus by scraping 84 Modern Standard Arabic sources from 19 countries, mainly national news agencies and major newspapers. They kept only political, international and local sections. On top of this corpus they trained BERT-style masked-language models, including a variant that continues pre-training from AraBERT. These were compared with BERT, Multilingual BERT and AraBERT on NER and binary political-conflict classification. The domain models win clearly and with statistical significance on politically flavoured datasets, but show no significant advantage on generic data. This gives analysts and political scientists a specialized tool for reading conflict news directly in Arabic.

## Key points
- The corpus covers 84 sources across 19 countries, 11.5GB of text in total. Native speakers curated the sources to keep them in MSA. Sections on Culture, Entertainment, Economy, Business and Sports were excluded.
- Every scraper had to be written for its specific site, because some sites serve static pages and others generate stories dynamically. Thread counts and sleep intervals were tuned by trial and error to get past site security measures.
- On NER, the models beat AraBERT and Multilingual BERT on AnerCORP, LinCE and Polyglot at p\<0.01. On the generic Aqmar set the difference was not significant (p>0.1).
- Binary classification used six datasets: four from news, one from social media and one from fact-checking (Arafacts). The ConfliBERT-Arabic Continual model, built on AraBERT, was best on all of them except one, where the uncased variant led.
- The domain models' largest margins came on social media (67.09 vs about 60–61 F1) and fact-checking (75.21 vs at most 72.83). Gains on SANAD AlKhaleej were only marginal. The raw corpus cannot be released for copyright reasons.

## Interesting ideas
- Pair the in-domain benchmarks with a generic dataset where no gain is expected, so a significant win can be read as a domain effect rather than general model strength.
- Filter the pre-training corpus by the source's own section metadata instead of a content classifier, which gives a cheap and transparent domain selector.
- Build a new binary task by relabeling an existing multi-topic corpus as "target topic vs everything else," so no fresh annotation is needed.
- Continue pre-training from a strong general model rather than training from scratch, and report cased and uncased variants side by side.
- Run morphological segmentation (Farasa in AraBERT) before subword vocabulary learning, so that forms with and without an affix such as the article do not become duplicate tokens.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Every claimed improvement to a domain-adapted component could ship with an out-of-domain control set. A significant gain there would flag leakage or an evaluation artefact.
- **I2** (learn): When collecting raw text for silver data, site sections or genre tags could route documents into domain-specific pools before any witness parser runs.
- **I3** (answer): Existing topic-labelled English collections could be turned into cheap yes/no relevance tests that probe whether the world model picks up what a text is about.
- **I4** (tree): gparse could be adapted to a new domain by further training from its general checkpoint, with explicit comparisons of variants per dataset.
- **I5** (tokens): Splitting English clitics and affixes in a linguistically informed way before any learned subword step could keep lemma and morphology features consistent and explainable.

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

## Institutions

- [The University of Texas at Dallas](https://md.research.elamur.ai/free/en/universities/the-university-of-texas-at-dallas/) — University, Richardson, United States · 61 works · 5,614 citations
- [University of Arizona](https://md.research.elamur.ai/free/en/universities/university-of-arizona/) — University, Tucson, United States · 38 works · 10,603 citations
- [West Virginia University](https://md.research.elamur.ai/free/en/universities/west-virginia-university/) — University, Morgantown, United States · 2 works · 21 citations

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## Cite

Cite as: Sultan Alsarra, Luay Abdeljaber, Wooseong Yang, Niamat Zawad, Latifur Khan, Patrick Brandt, Javier Osorio and Vito D'Orazio. 2023. ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence. Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing.

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

```bibtex
@inproceedings{alsarra2023conflibert,
  title = {{ConfliBERT-Arabic: A Pre-trained Arabic Language Model for Politics, Conflicts and Violence}},
  author = {Sultan Alsarra, Luay Abdeljaber, Wooseong Yang, Niamat Zawad, Latifur Khan, Patrick Brandt, Javier Osorio and Vito D'Orazio},
  booktitle = {Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing},
  year = {2023},
  doi = {10.26615/978-954-452-092-2\_011},
  url = {https://doi.org/10.26615/978-954-452-092-2_011}
}
```

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*Updated 1 November 2026*