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

arXiv: https://arxiv.org/abs/2307.06218 · HTML: https://mlsanity.com/paper/2307.06218/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 53 references that are in the map.
- First posted on arXiv 12 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://mlsanity.com/paper/1907.12461/) (2019)
- [AraGPT2: Pre-Trained Transformer for Arabic Language Generation](https://mlsanity.com/paper/2012.15520/) (2020)
- [1.5 billion words Arabic Corpus](https://mlsanity.com/paper/1611.04033/) (2016)
- [Arabic Text Diacritization Using Deep Neural Networks](https://mlsanity.com/paper/1905.01965/) (2019)
- [Learning meters of Arabic and English poems with Recurrent Neural Networks: a step forward for language understanding and synthesis](https://mlsanity.com/paper/1905.05700/) (2019)

## Built on by (most cited first, 1 of 1)

- [POEMetric: The Last Stanza of Humanity](https://mlsanity.com/paper/2604.03695/) (2026)

## Abstract (arXiv)

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 textitAshaar 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 textitArudi 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.