On the importance of Data Scale in Pretraining Arabic Language Models
Abbas Ghaddar Philippe Langlais Mehdi Rezagholizadeh Boxing Chen
Pretraining monolingual language models have been proven to be vital for
performance in Arabic Natural Language Processing (NLP) tasks. In this paper,
we conduct a comprehensive study on the role of data in Arabic Pretrained
Language Models (PLMs). More precisely, we reassess the performance of a suite
of state-of-the-art Arabic PLMs by retraining them on massive-scale,
high-quality Arabic corpora. We have significantly improved the performance of
the leading Arabic encoder-only BERT-base and encoder-decoder T5-base models on
the ALUE and ORCA leaderboards, thereby reporting state-of-the-art results in
their respective model categories. In addition, our analysis strongly suggests
that pretraining data by far is the primary contributor to performance,
surpassing other factors. Our models and source code are publicly available at
https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/JABER-PyTorch.