4FACTORS — Palestinian Levantine Conversational Sample
50 native-written question–answer pairs in spoken Palestinian Levantine Arabic, each with an English gloss. This is a public demonstration sample from 4FACTORS, a producer of native, human-verified Arabic training data.
What this is
Real conversational exchanges — the kind of thing people actually say in shops, clinics, taxis, and at home — written from scratch by a first-language Palestinian speaker. Every line was reviewed against a written checklist before release.
- Not scraped from the web.
- Not machine-translated from English.
- Not synthetically generated by a language model.
For a producer of training data in 2026, provenance is the whole point: synthetic Arabic is free and abundant, and models trained on model output degrade. What has value is data a human actually wrote — and can be shown to have written.
Fields
| Field | Description |
|---|---|
id |
Stable identifier (pal-lev-NNN) |
question_ar |
The question turn, in Palestinian Levantine Arabic |
answer_ar |
The answer turn, in Palestinian Levantine Arabic |
translation_en |
Plain English gloss of both turns, separated by / |
domain |
Everyday setting (shopping, health, transport, government, social, …) |
variety |
Arabic variety — Palestinian Levantine throughout |
Example
| question_ar | answer_ar | translation_en | domain |
|---|---|---|---|
| السلام عليكم، بغلّبك، في عندك خبز وجبنة بيضاء؟ | اه في، كم بدك خبز وجبنة؟ | Hello, sorry to bother you — do you have bread and white cheese? / Yes, we do. How much do you want? | Shopping |
| دكتور، في خافض حرارة للولد؟ | اه، في. بس قديش عمر الولد؟ | Doctor, do you have a fever reducer for my child? / Yes, we do. But how old is the child? | Health |
How it was made
- Scoped brief in the writer's own dialect, with explicit prohibitions on copying, translating, and AI generation.
- Native composition — the speaker writes from lived situations, not from prompts scraped off the web.
- Review against a checklist — no real names, no real businesses, no copyrighted text, honest domain labels, natural spoken register.
- Normalisation and English glosses added by the 4FACTORS team.
The full 150-item public sample (Palestinian Levantine, Egyptian, and Modern Standard Arabic), including the reviewer's correction log for the Egyptian set, is described at the project page below. All three varieties are published on Hugging Face and Kaggle under CC BY-NC 4.0.
Scope and limitations
- 50 items — a demonstration sample, not a training-scale corpus.
- Single writer, single variety. Not balanced for speaker demographics.
- English glosses are plain functional translations for evaluation, not literary translation.
Commissioned datasets
4FACTORS collects Arabic data to a client's schema, in the varieties and volumes they need — Levantine, Egyptian, Gulf, Maghrebi, MSA, and others on request. Commissioned datasets are delivered with full IP transfer and English glosses included.
- Project page: https://www.4factors.ch/arabic-nlp-training-data/
- Request a custom dataset: https://www.4factors.ch/contact/
Licence
Released under CC BY-NC 4.0 — free to evaluate and use non-commercially, with attribution. Commercial training use is available via a commissioned or licensed dataset; contact us. All contributors are compensated and have consented to publication. No personal data. No real names, brands, or copyrighted source text.
Archived deposit
This sample is part of an archived corpus (all three varieties, 150 items) published on Zenodo together with the full provenance record — contributor brief, review process and SHA-256 checksums:
Citation
@dataset{4factors_arabic_2026,
author = {Poljanc, Andrej and {4FACTORS GmbH}},
title = {4FACTORS Arabic Native-Written Sample Corpus: Modern Standard
Arabic, Palestinian Levantine and Egyptian (150 items)},
year = {2026},
version = {1.0},
publisher = {Zenodo},
doi = {10.5281/zenodo.21380048},
url = {https://doi.org/10.5281/zenodo.21380048},
note = {Native-written, human-verified. CC BY-NC 4.0.}
}
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