Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation
This work addresses the problem of limited Arabic NLG resources for researchers and developers, though it is incremental as it builds upon existing Transformer architectures.
The authors tackled the lack of a comprehensive Arabic natural language generation toolkit by developing AraT5v2, a novel Arabic text-to-text Transformer model trained on extensive data with a 2,048-token sequence length, which outperformed competitive baselines by large margins, and they released Octopus, a Python-based toolkit for eight Arabic generation tasks using a single model.
Understanding Arabic text and generating human-like responses is a challenging endeavor. 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 novel Arabic text-to-text Transformer model, namely AraT5v2. Our new model is 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 release the models and the toolkit on our public repository.