Datasets:
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First Name: text
Last Name: text
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configs:
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data_files:
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license: cc-by-sa-4.0
language:
- ar
pretty_name: BAREC-LexBench
size_categories:
- 10K<n<100K
BAREC-LexBench
Benchmark Summary
BAREC-LexBench is a benchmark for Arabic lemma-level readability assessment, designed to evaluate readability lexicons and computational models independently of their granularity levels. It is built through a large-scale pairwise comparison task, where annotators determine which of two Arabic lemmas is easier to read and understand. It consists of 36K annotated pairs covering open-class lemmas from four parts-of-speech: adjectives, nouns, proper nouns, and verbs.
Languages
- Arabic (Modern Standard Arabic)
Dataset Structure
BAREC-LexBench compares lemmas across:
- Four same-POS categories: adjectives, nouns, verbs, and proper nouns.
- One corss-POS category: compares lemmas from different parts-of-speech.
This dataset consists of 6 subsets: Adj (7.2K pairs), Noun (7.2K pairs), Verb (7.2K pairs), Prop (7.2K pairs), Cross (7.2K pairs), and All (36K pairs). Each subsets has four splits: train (50%), tune (12.5%), dev (12.5%), and test (25%).
Data Instances
{'Lex1': 'داخِل', 'Lex1_dediac': 'داخل', 'POS1': 'adj', 'POS1_ar': 'صفة', 'POS1_en': 'adjective', 'Gloss1': 'entering', 'Lex2': 'فُوسْفُورِيّ', 'Lex2_dediac': 'فوسفوري', 'POS2': 'adj', 'POS2_ar': 'صفة', 'POS2_en': 'adjective', 'Gloss2': 'phosphorous', 'Set': 'Set1', 'Split': 'Train', 'POS': 'adj', 'label': '1', 'Answer': 'A'}
Data Fields
- Lex1: The first lemma, with diacritics.
- Lex1_dediac: The first lemma without diacritics.
- POS1: Part of speech of the first lemma.
- POS1_ar: Part of speech of the first lemma in Arabic.
- POS1_en: Part of speech of the first lemma in English.
- Gloss1: English gloss of the first lemma.
- Lex2: The second Arabic lemma, with diacritics.
- Lex2_dediac: The second lemma without diacritics.
- POS2: Part of speech of the second lemma.
- POS2_ar: Part of speech of the second lemma in Arabic.
- POS2_en: Part of speech of the second lemma in English.
- Gloss2: English gloss of the second lemma.
- Set: Dataset subset identifier (
Set1, ...,Set8). - Split: Dataset split (
Train,Tune,Dev, orTest). - POS: Part of speech category for the two lemmas being compared (
adj,noun,verb,noun_prop, orcross). - label: Label indicating which lemma is easier to read (
1,2, or0if equal). - Answer: One-to-one mapping from label (
{'1': 'A', '2': 'B', '0':'C'}).
Citation
If you use BAREC-LexBench in your work, please cite the following paper:
@inproceedings{elmadani-etal-2026-barec-lex,
title = "A Benchmark for Arabic Lemma-Level Readability Assessment",
author = "Elmadani, Khalid N. and Saeed, Mostafa and Taha-Thomure, Hanada and Habash, Nizar",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics"
}