Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models

Mostafa Saeed; Nizar Habash

Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models

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Abstract

Lemmatization for dialectal Arabic poses many challenges due to the lack of orthographic standards and limited morphological analyzers. This work explores the effectiveness of Seq2Seq models for lemmatizing dialectal Arabic, both without analyzers and with their integration. We assess how well these models generalize across dialects and benefit from related varieties. Focusing on Egyptian, Gulf, and Levantine dialects with varying resource levels, our analysis highlights both the potential and limitations of data-driven approaches. The proposed method achieves significant gains over baselines, performing well in both low-resource and dialect-rich scenarios.

Anthology ID:: 2025.arabicnlp-main.10
Volume:: Proceedings of The Third Arabic Natural Language Processing Conference
Month:: November
Year:: 2025
Address:: Suzhou, China
Editors:: Kareem Darwish, Ahmed Ali, Ibrahim Abu Farha, Samia Touileb, Imed Zitouni, Ahmed Abdelali, Sharefah Al-Ghamdi, Sakhar Alkhereyf, Wajdi Zaghouani, Salam Khalifa, Badr AlKhamissi, Rawan Almatham, Injy Hamed, Zaid Alyafeai, Areeb Alowisheq, Go Inoue, Khalil Mrini, Waad Alshammari
Venue:: ArabicNLP
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 117–129
Language:
URL:: https://aclanthology.org/2025.arabicnlp-main.10/
DOI:
Bibkey:
Cite (ACL):: Mostafa Saeed and Nizar Habash. 2025. Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models. In Proceedings of The Third Arabic Natural Language Processing Conference, pages 117–129, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):: Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models (Saeed & Habash, ArabicNLP 2025)
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PDF:: https://aclanthology.org/2025.arabicnlp-main.10.pdf

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@inproceedings{saeed-habash-2025-lemmatizing,
    title = "Lemmatizing Dialectal {A}rabic with Sequence-to-Sequence Models",
    author = "Saeed, Mostafa  and
      Habash, Nizar",
    editor = "Darwish, Kareem  and
      Ali, Ahmed  and
      Abu Farha, Ibrahim  and
      Touileb, Samia  and
      Zitouni, Imed  and
      Abdelali, Ahmed  and
      Al-Ghamdi, Sharefah  and
      Alkhereyf, Sakhar  and
      Zaghouani, Wajdi  and
      Khalifa, Salam  and
      AlKhamissi, Badr  and
      Almatham, Rawan  and
      Hamed, Injy  and
      Alyafeai, Zaid  and
      Alowisheq, Areeb  and
      Inoue, Go  and
      Mrini, Khalil  and
      Alshammari, Waad",
    booktitle = "Proceedings of The Third Arabic Natural Language Processing Conference",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.arabicnlp-main.10/",
    pages = "117--129",
    ISBN = "979-8-89176-352-4",
    abstract = "Lemmatization for dialectal Arabic poses many challenges due to the lack of orthographic standards and limited morphological analyzers. This work explores the effectiveness of Seq2Seq models for lemmatizing dialectal Arabic, both without analyzers and with their integration. We assess how well these models generalize across dialects and benefit from related varieties. Focusing on Egyptian, Gulf, and Levantine dialects with varying resource levels, our analysis highlights both the potential and limitations of data-driven approaches. The proposed method achieves significant gains over baselines, performing well in both low-resource and dialect-rich scenarios."
}

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%0 Conference Proceedings
%T Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models
%A Saeed, Mostafa
%A Habash, Nizar
%Y Darwish, Kareem
%Y Ali, Ahmed
%Y Abu Farha, Ibrahim
%Y Touileb, Samia
%Y Zitouni, Imed
%Y Abdelali, Ahmed
%Y Al-Ghamdi, Sharefah
%Y Alkhereyf, Sakhar
%Y Zaghouani, Wajdi
%Y Khalifa, Salam
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Hamed, Injy
%Y Alyafeai, Zaid
%Y Alowisheq, Areeb
%Y Inoue, Go
%Y Mrini, Khalil
%Y Alshammari, Waad
%S Proceedings of The Third Arabic Natural Language Processing Conference
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-352-4
%F saeed-habash-2025-lemmatizing
%X Lemmatization for dialectal Arabic poses many challenges due to the lack of orthographic standards and limited morphological analyzers. This work explores the effectiveness of Seq2Seq models for lemmatizing dialectal Arabic, both without analyzers and with their integration. We assess how well these models generalize across dialects and benefit from related varieties. Focusing on Egyptian, Gulf, and Levantine dialects with varying resource levels, our analysis highlights both the potential and limitations of data-driven approaches. The proposed method achieves significant gains over baselines, performing well in both low-resource and dialect-rich scenarios.
%U https://aclanthology.org/2025.arabicnlp-main.10/
%P 117-129

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Markdown (Informal)

[Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models](https://aclanthology.org/2025.arabicnlp-main.10/) (Saeed & Habash, ArabicNLP 2025)

Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models (Saeed & Habash, ArabicNLP 2025)

ACL

Mostafa Saeed and Nizar Habash. 2025. Lemmatizing Dialectal Arabic with Sequence-to-Sequence Models. In Proceedings of The Third Arabic Natural Language Processing Conference, pages 117–129, Suzhou, China. Association for Computational Linguistics.