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arXiv · 2112.04329

JABER and SABER: Junior and Senior Arabic BERt

Abbas Ghaddar·Yimeng Wu·Ahmad Rashid·Khalil Bibi·Mehdi Rezagholizadeh·Chao Xing·Yasheng Wang·Duan Xinyu·Zhefeng Wang·Baoxing Huai·Xin Jiang·Qun Liu·Philippe Langlais
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Abstract

Language-specific pre-trained models have proven to be more accurate than multilingual ones in a monolingual evaluation setting, Arabic is no exception. However, we found that previously released Arabic BERT models were significantly under-trained. In this technical report, we present JABER and SABER, Junior and Senior Arabic BERt respectively, our pre-trained language model prototypes dedicated for Arabic. We conduct an empirical study to systematically evaluate the performance of models across a diverse set of existing Arabic NLU tasks. Experimental results show that JABER and SABER achieve state-of-the-art performances on ALUE, a new benchmark for Arabic Language Understanding Evaluation, as well as on a well-established NER benchmark.

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Abbas Ghaddar, Yimeng Wu, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh, Chao Xing, Yasheng Wang, Duan Xinyu, Zhefeng Wang, Baoxing Huai, Xin Jiang, Qun Liu, Philippe Langlais. 2022-01-09. JABER and SABER: Junior and Senior Arabic BERt. https://arxiv.org/abs/2112.04329

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