JABER: Junior Arabic BERt

• URL: http://arxiv.org/abs/2112.04329v1
• Date: Wed, 8 Dec 2021 15:19:24 GMT
• Title: JABER: Junior Arabic BERt
• Authors: Abbas Ghaddar, Yimeng Wu, Ahmad Rashid, Khalil Bibi, Mehdi Rezagholizadeh, Chao Xing, Yasheng Wang, Duan Xinyu, Zhefeng Wang, Baoxing Huai, Xin Jiang, Qun Liu and Philippe Langlais
• Abstract summary: We present JABER, Junior Arabic BERt, our pretrained language model prototype dedicated for Arabic. We conduct an empirical study to systematically evaluate the performance of models across a diverse set of existing Arabic NLU tasks.
• Score: 37.174723137868675
• License: http://creativecommons.org/licenses/by/4.0/
• 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, Junior Arabic BERt, our pretrained language model prototype 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 achieves the 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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