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Authors: Mohamed El Ghaly Beheitt 1 and Moez Ben Haj Hmida 2

Affiliations: 1 LIPAH-FST Laboratory , Faculty of Sciences of Tunis, University of Tunis El Manar , Tunis, Tunisia ; 2 National Engineering School of Tunis, University of Tunis El Manar, Tunis, Tunisia

Keyword(s): Deep Learning, Transformer, Natural Language Processing, GPT-2, Arabic Poem.

Abstract: Automatically generating poetry by computers is a challenging topic that requires the use of advanced deep learning techniques. While much attention has been given to English and Chinese poem generation, there are few significant efforts considering other languages. Generating poems in Arabic is a difficult task due to the complexity of the Arabic language grammatical structure. In this paper, we investigate the feasibility of training generative pre-trained language model GPT-2 to generate Arabic poems. The results of the experiments, which included the BLEU score as well as human assessments, confirmed the effectiveness of our proposed model. Both automatic and human evaluations show that our proposed model outperforms existing models in generating Arabic poetry.

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Paper citation in several formats:
Beheitt, M. and Ben Haj Hmida, M. (2022). Automatic Arabic Poem Generation with GPT-2. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 366-374. DOI: 10.5220/0010847100003116

@conference{icaart22,
author={Mohamed El Ghaly Beheitt. and Moez {Ben Haj Hmida}.},
title={Automatic Arabic Poem Generation with GPT-2},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={366-374},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010847100003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Automatic Arabic Poem Generation with GPT-2
SN - 978-989-758-547-0
IS - 2184-433X
AU - Beheitt, M.
AU - Ben Haj Hmida, M.
PY - 2022
SP - 366
EP - 374
DO - 10.5220/0010847100003116
PB - SciTePress