Diacritization of Arabic text is both an interesting and a challenging problem at the same
time with various applications ranging from speech synthesis to helping students learning …

Arabic diacritics are signs used in Arabic orthography to represent essential
morphophonological and syntactic information. It is a common practice to leave out those …

In order to accurately represent the meaning and pronunciation of Arabic words and
sentences, the presence of diacritics plays a crucial role. Over the years, researchers have …

Deep learning has emerged as a new area of machine learning research. It is an approach
that can learn features and hierarchical representation purely from data and has been …

Automating natural language understanding is a lifelong quest addressed for decades. With
the help of advances in machine learning and particularly, deep learning, we are able to …

We propose a novel architecture for labelling character sequences that achieves state-of-the-
art results on the Tashkeela Arabic diacritization benchmark. The core is a two-level …

In this work, we present several deep learning models for the automatic diacritization of
Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural …

Automatic diacritization of Arabic text is a pivotal process that involves the addition of
diacritical marks to enhance the clarity and comprehension of the text. This task is crucial for …

Arabic is mostly written now without its diacritics (short vowels). Adding these diacritics
decreases reading ambiguity among other benefits. This work aims to develop a fast and …

Diacritization plays a pivotal role in improving readability and disambiguating the meaning
of Arabic texts. Efforts have so far focused on marking every eligible character (Full …