TY - GEN
T1 - Syllable Structures Across Arabic Varieties
AU - Qaddoumi, Abdelrahim
AU - Kodner, Jordan
AU - Khalifa, Salam
AU - Broselow, Ellen
AU - Rambow, Owen
N1 - Publisher Copyright:
© 2026 Association for Computational Linguistics.
PY - 2026
Y1 - 2026
N2 - This study compares the syllable structures of nine Arabic varieties from Wiktionary, using a computational syllabifier. It further investigates methods for learning syllable boundaries in unsyllabified words transcribed in the International Phonetic Alphabet (IPA). The syllabification algorithm is evaluated under three conditions: (i) Default, employing fixed rules; (ii) Joint, learning onsets and codas across all varieties collectively; and (iii) Per-variety, learning onsets and codas specific to each variety. Results indicate that the default configuration yields the highest accuracy, ranging from 97.05% to 100%. The per-variety approach achieves 90.64% to 100% accuracy, while the joint approach ranges from 84.63% to 94.74%. A cross-variety analysis using Jensen-Shannon divergence reveals three principal groupings: Egyptian, Hejazi, and Modern Standard Arabic are closely related; Levantine and Gulf varieties constitute a second cluster; and Juba Arabic, Maltese, and Moroccan emerge as outliers. A cleaned dataset encompassing all nine varieties is also provided.
AB - This study compares the syllable structures of nine Arabic varieties from Wiktionary, using a computational syllabifier. It further investigates methods for learning syllable boundaries in unsyllabified words transcribed in the International Phonetic Alphabet (IPA). The syllabification algorithm is evaluated under three conditions: (i) Default, employing fixed rules; (ii) Joint, learning onsets and codas across all varieties collectively; and (iii) Per-variety, learning onsets and codas specific to each variety. Results indicate that the default configuration yields the highest accuracy, ranging from 97.05% to 100%. The per-variety approach achieves 90.64% to 100% accuracy, while the joint approach ranges from 84.63% to 94.74%. A cross-variety analysis using Jensen-Shannon divergence reveals three principal groupings: Egyptian, Hejazi, and Modern Standard Arabic are closely related; Levantine and Gulf varieties constitute a second cluster; and Juba Arabic, Maltese, and Moroccan emerge as outliers. A cleaned dataset encompassing all nine varieties is also provided.
UR - https://www.scopus.com/pages/publications/105042262615
U2 - 10.18653/v1/2026.vardial-1.21
DO - 10.18653/v1/2026.vardial-1.21
M3 - Conference contribution
AN - SCOPUS:105042262615
T3 - VarDial 2026 - 13th Workshop on NLP for Similar Languages, Varieties and Dialects, Proceedings of the Workshop
SP - 250
EP - 260
BT - VarDial 2026 - 13th Workshop on NLP for Similar Languages, Varieties and Dialects, Proceedings of the Workshop
A2 - Scherrer, Yves
A2 - Aepli, Noemi
A2 - Blaschke, Verena
A2 - Jauhiainen, Tommi
A2 - Ljubesic, Nikola
A2 - Nakov, Preslav
A2 - Tiedemann, Jorg
A2 - Zampieri, Marcos
PB - Association for Computational Linguistics (ACL)
T2 - 13th Workshop on NLP for Similar Languages, Varieties and Dialects, VarDial 2026 - co-located with the 19th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2026
Y2 - 29 March 2026
ER -