Ashaar: Automatic Analysis and Generation of Arabic Poetry Using Deep Learning Approaches

• URL: http://arxiv.org/abs/2307.06218v1
• Date: Wed, 12 Jul 2023 15:07:16 GMT
• Title: Ashaar: Automatic Analysis and Generation of Arabic Poetry Using Deep Learning Approaches
• Authors: Zaid Alyafeai and Maged S. Al-Shaibani and Moataz Ahmed
• Abstract summary: This paper introduces a framework called textitAshaar, 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. As part of this endeavor, we provide four datasets: one for poetry generation, another for diacritization, and two for Arudi-style prediction.
• Score: 7.021140304091526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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