 Citation ```bibtex @inproceedings{inoue-etal-2021-interplay,     title = "The Interplay of Variant, Size, and Task Type in {A}rabic Pre-trained Language Models",     author = "Inoue, Go  and       Alhafni, Bashar  and       Baimukan, Nurpeiis  and       Bouamor, Houda  and       Habash, Nizar",     booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",     month = apr,     year = "2021",     address = "Kyiv, Ukraine (Online)",     publisher = "Association for Computational Linguistics",     abstract = "In this paper, we explore the effects of language variants, data sizes, and fine-tuning task types in Arabic pre-trained language models. To do so, we build three pre-trained language models across three variants of Arabic: Modern Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a fourth language model which is pre-trained on a da of the three. We also examine the importance of pre-training data size by building additional models that are pre-trained on a scaled-down set of the MSA variant. We compare our different models to each other, as well as to eight publicly available models by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest that the variant proximity of pre-training data to fine-tuning data is more important than the pre-training data size. We exploit this insight in defining an optimized system selection model for the studied tasks.", } ```