2022
EMNLP
EMNLP 2022
Gulf Arabic Diacritization: Guidelines, Initial Dataset, and Results
Abstract
AbstractArabic diacritic recovery is important for a variety of downstream tasks such as text-to-speech. In this paper, we introduce a new Gulf Arabic diacritization dataset composed of 19,850 words based on a subset of the Gumar corpus. We provide comprehensive set of guidelines for diacritization to enable the diacritization of more data. We also report on diacritization results based on the new corpus using a Hidden Markov Model and character-based sequence to sequence models.
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Mega-Team
- 22 authors
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Interdisciplinary Bridge
- Artificial Intelligence and Healthcare & Medicine
Authors
Nouf Alabbasi,
Mohamed Al-badrashiny,
Maryam Aldahmani,
Ahmed AlDhanhani,
Abdullah Saleh Alhashmi,
Fawaghy Ahmed Alhashmi,
Khalid Al Hashemi,
Rama Emad Alkhobbi,
Shamma T Al Maazmi,
Mohammed Ali Alyafeai,
Mariam M Alzaabi,
Mohamed Saqer Alzaabi,
Fatma Khalid Badri,
Kareem Darwish,
Ehab Mansour Diab,
Muhammad Morsy Elmallah,
Amira Ayman Elnashar,
Ashraf Hatim Elneima,
MHD Tameem Kabbani,
Nour Rabih,
Ahmad Saad,
Ammar Mamoun Sousou