Align-Extract-Score: Graded Relevance of Arabic Essays

Marwan Sayed; Salam Albatarni; Reem Suwaileh; Tamer Elsayed

Align-Extract-Score: Graded Relevance of Arabic Essays

Marwan Sayed, Salam Albatarni, Reem Suwaileh, Tamer Elsayed

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Abstract

Relevance is a central component of Automated Essay Scoring (AES), as it measures how well a student essay addresses a given writing prompt. In this work, we propose AESER, a novel approach to address the problem of graded relevance scoring in Arabic cross-prompt AES. AESER adapts the given prompt more towards the writing space of essays and computes token-level prompt-essay similarity matrices from contextual embeddings. These matrices are processed by a convolutional neural network to predict the relevance score. Experiments on a large Arabic AES dataset show that AESER outperforms strong baselines, establishing a new state-of-the-art performance.

Anthology ID:: 2026.arabicnlp-main.3
Volume:: Proceedings of The Fourth Arabic Natural Language Processing Conference
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Muhammad Abdul-Mageed, Tamer Elsayed, Houda Bouamor, Samhaa El-Beltagy, Hend Al-Khalifa, Ahmed Abdelali, AbdelRahim Elmadany, Abdellah El Mekki, Kareem Darwish, Sakhar Alkhereyf, Hamzah Luqman, Salam Khalifa, Samar Magdy, Zaid Alyafeai, Injy Hamed, Wajdi Zaghouani, Imed Zitouni
Venue:: ArabicNLP
SIG:: SIGARAB
Publisher:: Association for Computational Linguistics
Note:
Pages:: 22–32
Language:
URL:: https://aclanthology.org/2026.arabicnlp-main.3/
DOI:
Bibkey:
Cite (ACL):: Marwan Sayed, Salam Albatarni, Reem Suwaileh, and Tamer Elsayed. 2026. Align-Extract-Score: Graded Relevance of Arabic Essays. In Proceedings of The Fourth Arabic Natural Language Processing Conference, pages 22–32, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Align-Extract-Score: Graded Relevance of Arabic Essays (Sayed et al., ArabicNLP 2026)
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PDF:: https://aclanthology.org/2026.arabicnlp-main.3.pdf

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@inproceedings{sayed-etal-2026-align,
    title = "Align-Extract-Score: Graded Relevance of {A}rabic Essays",
    author = "Sayed, Marwan  and
      Albatarni, Salam  and
      Suwaileh, Reem  and
      Elsayed, Tamer",
    editor = "Abdul-Mageed, Muhammad  and
      Elsayed, Tamer  and
      Bouamor, Houda  and
      El-Beltagy, Samhaa  and
      Al-Khalifa, Hend  and
      Abdelali, Ahmed  and
      Elmadany, AbdelRahim  and
      El Mekki, Abdellah  and
      Darwish, Kareem  and
      Alkhereyf, Sakhar  and
      Luqman, Hamzah  and
      Khalifa, Salam  and
      Magdy, Samar  and
      Alyafeai, Zaid  and
      Hamed, Injy  and
      Zaghouani, Wajdi  and
      Zitouni, Imed",
    booktitle = "Proceedings of The Fourth {A}rabic Natural Language Processing Conference",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.arabicnlp-main.3/",
    pages = "22--32",
    ISBN = "979-8-89176-473-6",
    abstract = "Relevance is a central component of Automated Essay Scoring (AES), as it measures how well a student essay addresses a given writing prompt. In this work, we propose AESER, a novel approach to address the problem of graded relevance scoring in Arabic cross-prompt AES. AESER adapts the given prompt more towards the writing space of essays and computes token-level prompt-essay similarity matrices from contextual embeddings. These matrices are processed by a convolutional neural network to predict the relevance score. Experiments on a large Arabic AES dataset show that AESER outperforms strong baselines, establishing a new state-of-the-art performance."
}

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%0 Conference Proceedings
%T Align-Extract-Score: Graded Relevance of Arabic Essays
%A Sayed, Marwan
%A Albatarni, Salam
%A Suwaileh, Reem
%A Elsayed, Tamer
%Y Abdul-Mageed, Muhammad
%Y Elsayed, Tamer
%Y Bouamor, Houda
%Y El-Beltagy, Samhaa
%Y Al-Khalifa, Hend
%Y Abdelali, Ahmed
%Y Elmadany, AbdelRahim
%Y El Mekki, Abdellah
%Y Darwish, Kareem
%Y Alkhereyf, Sakhar
%Y Luqman, Hamzah
%Y Khalifa, Salam
%Y Magdy, Samar
%Y Alyafeai, Zaid
%Y Hamed, Injy
%Y Zaghouani, Wajdi
%Y Zitouni, Imed
%S Proceedings of The Fourth Arabic Natural Language Processing Conference
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-473-6
%F sayed-etal-2026-align
%X Relevance is a central component of Automated Essay Scoring (AES), as it measures how well a student essay addresses a given writing prompt. In this work, we propose AESER, a novel approach to address the problem of graded relevance scoring in Arabic cross-prompt AES. AESER adapts the given prompt more towards the writing space of essays and computes token-level prompt-essay similarity matrices from contextual embeddings. These matrices are processed by a convolutional neural network to predict the relevance score. Experiments on a large Arabic AES dataset show that AESER outperforms strong baselines, establishing a new state-of-the-art performance.
%U https://aclanthology.org/2026.arabicnlp-main.3/
%P 22-32

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Markdown (Informal)

[Align-Extract-Score: Graded Relevance of Arabic Essays](https://aclanthology.org/2026.arabicnlp-main.3/) (Sayed et al., ArabicNLP 2026)

Align-Extract-Score: Graded Relevance of Arabic Essays (Sayed et al., ArabicNLP 2026)

ACL

Marwan Sayed, Salam Albatarni, Reem Suwaileh, and Tamer Elsayed. 2026. Align-Extract-Score: Graded Relevance of Arabic Essays. In Proceedings of The Fourth Arabic Natural Language Processing Conference, pages 22–32, Budapest, Hungary. Association for Computational Linguistics.