# The Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness

arXiv: https://arxiv.org/abs/2508.17347 · HTML: https://mlsanity.com/paper/2508.17347/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2010.11334/) (2020)
- [NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2103.08466/) (2021)
- [NADI 2023: The Fourth Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2310.16117/) (2023)
- [ALDi: Quantifying the Arabic Level of Dialectness of Text](https://mlsanity.com/paper/2310.13747/) (2023)
- [NADI 2022: The Third Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2210.09582/) (2022)
- [NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2407.04910/) (2024)
- [Arabic Dialect Identification under Scrutiny: Limitations of Single-label Classification](https://mlsanity.com/paper/2310.13661/) (2023)
- [Exploiting Dialect Identification in Automatic Dialectal Text Normalization](https://mlsanity.com/paper/2407.03020/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learning the Arabic Dialect Continuum as a Continuous Space: A Regression Approach to Speaker Origin Prediction](https://mlsanity.com/paper/2607.19751/) (2026)

## Abstract (arXiv)

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.