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        <title>The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness</title>
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            <namePart type="given">Christos</namePart>
            <namePart type="family">Christodoulopoulos</namePart>
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    <abstract>Arabic dialects form a diverse continuum, yet NLP models often treat them as discrete categories. Recent work addresses this issue by modeling dialectness as a continuous variable, notably through the Arabic Level of Dialectness (ALDi). However, ALDi reduces complex variation to a single dimension. We propose a complementary measure: the Arabic Generality Score (AGS), which quantifies how widely a word is used across dialects. We introduce a pipeline that combines word alignment, etymology-aware edit distance, and smoothing to annotate a parallel corpus with word-level AGS. A regression model is then trained to predict AGS in context. Our approach outperforms strong baselines, including state-of-the-art dialect ID systems, on a multi-dialect benchmark. AGS offers a scalable, linguistically grounded way to model lexical generality, enriching representations of Arabic dialectness. Code is publicly available at https://github.com/CAMeL-Lab/arabic-generality-score.</abstract>
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%0 Conference Proceedings
%T The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness
%A Sha’ban, Sanad
%A Habash, Nizar
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F shaban-habash-2025-arabic
%X Arabic dialects form a diverse continuum, yet NLP models often treat them as discrete categories. Recent work addresses this issue by modeling dialectness as a continuous variable, notably through the Arabic Level of Dialectness (ALDi). However, ALDi reduces complex variation to a single dimension. We propose a complementary measure: the Arabic Generality Score (AGS), which quantifies how widely a word is used across dialects. We introduce a pipeline that combines word alignment, etymology-aware edit distance, and smoothing to annotate a parallel corpus with word-level AGS. A regression model is then trained to predict AGS in context. Our approach outperforms strong baselines, including state-of-the-art dialect ID systems, on a multi-dialect benchmark. AGS offers a scalable, linguistically grounded way to model lexical generality, enriching representations of Arabic dialectness. Code is publicly available at https://github.com/CAMeL-Lab/arabic-generality-score.
%U https://aclanthology.org/2025.emnlp-main.1524/
%P 29990-30001

Markdown (Informal)

[The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness](https://aclanthology.org/2025.emnlp-main.1524/) (Sha’ban & Habash, EMNLP 2025)

• The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness (Sha’ban & Habash, EMNLP 2025)

ACL

• Sanad Sha’ban and Nizar Habash. 2025. The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 29990–30001, Suzhou, China. Association for Computational Linguistics.