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

Understanding Arabic text and generating human-like responses is a challenging task. While many researchers have proposed models and solutions for individual problems, there is an acute shortage of a comprehensive Arabic natural language generation toolkit that is capable of handling a wide range of tasks. In this work, we present a robust Arabic text-to-text Transformer model, namely AraT5v2, methodically trained on extensive and diverse data, utilizing an extended sequence length of 2,048 tokens. We explore various pretraining strategies including unsupervised, supervised, and joint pertaining, under both single and multitask settings. Our models outperform competitive baselines with large margins. We take our work one step further by developing and publicly releasing OCTOPUS, a Python-based package and command-line toolkit tailored for eight Arabic generation tasks all exploiting a single model. We provide a link to the models and the toolkit through our public repository.