@inproceedings{bashendy-etal-2026-laila,
title = "{LAILA}: A Large Trait-Based Dataset for {A}rabic Automated Essay Scoring",
author = "Bashendy, May and
Massoud, Walid and
Eltanbouly, Sohaila and
Albatarni, Salam and
Sayed, Marwan and
Abir, Abrar and
Bouamor, Houda and
Elsayed, Tamer",
editor = "Demberg, Vera and
Inui, Kentaro and
Marquez, Llu{\'i}s",
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-long.142/",
pages = "3074--3091",
ISBN = "979-8-89176-380-7",
abstract = "Automated Essay Scoring (AES) has gained increasing attention in recent years, yet research on Arabic AES remains limited due to the lack of publicly available datasets. To address this, we introduce LAILA, the largest publicly available Arabic AES dataset to date, comprising 7,859 essays annotated with holistic and trait-specific scores on seven dimensions: relevance, organization, vocabulary, style, development, mechanics, and grammar. We detail the dataset design, collection, and annotations, and provide benchmark results using state-of-the-art Arabic and English models in prompt-specific and cross-prompt settings. LAILA fills a critical need in Arabic AES research, supporting the development of robust scoring systems."
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<abstract>Automated Essay Scoring (AES) has gained increasing attention in recent years, yet research on Arabic AES remains limited due to the lack of publicly available datasets. To address this, we introduce LAILA, the largest publicly available Arabic AES dataset to date, comprising 7,859 essays annotated with holistic and trait-specific scores on seven dimensions: relevance, organization, vocabulary, style, development, mechanics, and grammar. We detail the dataset design, collection, and annotations, and provide benchmark results using state-of-the-art Arabic and English models in prompt-specific and cross-prompt settings. LAILA fills a critical need in Arabic AES research, supporting the development of robust scoring systems.</abstract>
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%0 Conference Proceedings
%T LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring
%A Bashendy, May
%A Massoud, Walid
%A Eltanbouly, Sohaila
%A Albatarni, Salam
%A Sayed, Marwan
%A Abir, Abrar
%A Bouamor, Houda
%A Elsayed, Tamer
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F bashendy-etal-2026-laila
%X Automated Essay Scoring (AES) has gained increasing attention in recent years, yet research on Arabic AES remains limited due to the lack of publicly available datasets. To address this, we introduce LAILA, the largest publicly available Arabic AES dataset to date, comprising 7,859 essays annotated with holistic and trait-specific scores on seven dimensions: relevance, organization, vocabulary, style, development, mechanics, and grammar. We detail the dataset design, collection, and annotations, and provide benchmark results using state-of-the-art Arabic and English models in prompt-specific and cross-prompt settings. LAILA fills a critical need in Arabic AES research, supporting the development of robust scoring systems.
%U https://aclanthology.org/2026.eacl-long.142/
%P 3074-3091
Markdown (Informal)
[LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring](https://aclanthology.org/2026.eacl-long.142/) (Bashendy et al., EACL 2026)
ACL
- May Bashendy, Walid Massoud, Sohaila Eltanbouly, Salam Albatarni, Marwan Sayed, Abrar Abir, Houda Bouamor, and Tamer Elsayed. 2026. LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3074–3091, Rabat, Morocco. Association for Computational Linguistics.