# AraPoemBERT: A Pretrained Language Model for Arabic Poetry Analysis

arXiv: https://arxiv.org/abs/2403.12392 · HTML: https://mlsanity.com/paper/2403.12392/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [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)
- [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, 8 of 8)

- [A Survey of Large Language Models for Arabic Language and its Dialects](https://mlsanity.com/paper/2410.20238/) (2024)
- [EgyBERT: A Large Language Model Pretrained on Egyptian Dialect Corpora](https://mlsanity.com/paper/2408.03524/) (2024)
- [Large Language Models and Arabic Content: A Review](https://mlsanity.com/paper/2505.08004/) (2025)
- [A Rhythm-Aware Phrase Insertion for Classical Arabic Poetry Composition](https://mlsanity.com/paper/2509.18514/) (2025)
- [Tarab: A Multi-Dialect Corpus of Arabic Lyrics and Poetry](https://mlsanity.com/paper/2603.16601/) (2026)
- [Instruction-Guided Poetry Generation in Arabic and Its Dialects](https://mlsanity.com/paper/2604.27766/) (2026)
- [PARSI: Persian Authorship Recognition via Stylometric Integration](https://mlsanity.com/paper/2506.21840/) (2025)
- [Echoes Across Centuries: Phonetic Signatures of Persian Poets](https://mlsanity.com/paper/2603.14443/) (2026)

## Abstract (arXiv)

Arabic poetry, with its rich linguistic features and profound cultural significance, presents a unique challenge to the Natural Language Processing (NLP) field. The complexity of its structure and context necessitates advanced computational models for accurate analysis. In this paper, we introduce AraPoemBERT, an Arabic language model pretrained exclusively on Arabic poetry text. To demonstrate the effectiveness of the proposed model, we compared AraPoemBERT with 5 different Arabic language models on various NLP tasks related to Arabic poetry. The new model outperformed all other models and achieved state-of-the-art results in most of the downstream tasks. AraPoemBERT achieved unprecedented accuracy in two out of three novel tasks: poet's gender classification (99.34% accuracy), and poetry sub-meter classification (97.79% accuracy). In addition, the model achieved an accuracy score in poems' rhyme classification (97.73% accuracy) which is almost equivalent to the best score reported in this study. Moreover, the proposed model significantly outperformed previous work and other comparative models in the tasks of poems' sentiment analysis, achieving an accuracy of 78.95%, and poetry meter classification (99.03% accuracy), while significantly expanding the scope of these two problems. The dataset used in this study, contains more than 2.09 million verses collected from online sources, each associated with various attributes such as meter, sub-meter, poet, rhyme, and topic. The results demonstrate the effectiveness of the proposed model in understanding and analyzing Arabic poetry, achieving state-of-the-art results in several tasks and outperforming previous works and other language models included in the study. AraPoemBERT model is publicly available on urlhttps://huggingface.co/faisalq.