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Understanding Arabic text and generating human-like responses is achallenging endeavor. While many researchers have proposed models and solutionsfor individual problems, there is an acute shortage of a comprehensive Arabicnatural language generation toolkit that is capable of handling a wide range oftasks. In this work, we present a novel Arabic text-to-text Transformer model,namely AraT5v2. Our new model is methodically trained on extensive and diversedata, utilizing an extended sequence length of 2,048 tokens. We explore variouspretraining strategies including unsupervised, supervised, and jointpertaining, under both single and multitask settings. Our models outperformcompetitive baselines with large margins. We take our work one step further bydeveloping and publicly releasing Octopus, a Python-based package andcommand-line toolkit tailored for eight Arabic generation tasks all exploitinga single model. We release the models and the toolkit on our public repository.

Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation