Ashaar: Automatic Analysis and Generation of Arabic Poetry Using Deep
  Learning Approaches
Zaid Alyafeai Maged S. Al-Shaibani Moataz Ahmed
Poetry holds immense significance within the cultural and traditional fabric
of any nation. It serves as a vehicle for poets to articulate their emotions,
preserve customs, and convey the essence of their culture. Arabic poetry is no
exception, having played a cherished role in the heritage of the Arabic
community throughout history and maintaining its relevance in the present era.
Typically, comprehending Arabic poetry necessitates the expertise of a linguist
who can analyze its content and assess its quality. This paper presents the
introduction of a framework called \textit{Ashaar}
https://github.com/ARBML/Ashaar, which encompasses a collection of datasets and
pre-trained models designed specifically for the analysis and generation of
Arabic poetry. The pipeline established within our proposed approach
encompasses various aspects of poetry, such as meter, theme, and era
classification. It also incorporates automatic poetry diacritization, enabling
more intricate analyses like automated extraction of the \textit{Arudi} style.
Additionally, we explore the feasibility of generating conditional poetry
through the pre-training of a character-based GPT model. Furthermore, as part
of this endeavor, we provide four datasets: one for poetry generation, another
for diacritization, and two for Arudi-style prediction. These datasets aim to
facilitate research and development in the field of Arabic poetry by enabling
researchers and enthusiasts to delve into the nuances of this rich literary
tradition.