Fawaghy Ahmed Alhashmi

Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here

2022

pdf bib
Gulf Arabic Diacritization: Guidelines, Initial Dataset, and Results
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
Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)

Arabic 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.
Search
Co-authors
Venues
Fix data