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AraBERT:Transformer-based Model for Arabic Language Understanding - 2020

Arabert:Transformer-Based Model For Arabic Language Understanding

Research Area:  Machine Learning


The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA), have proven to be very challenging to tackle. Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus. Such models were able to set new standards and achieve state-of-the-art results for most NLP tasks. In this paper, we pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language. The performance of AraBERT is compared to multilingual BERT from Google and other state-of-the-art approaches. The results showed that the newly developed AraBERT achieved state-of-the-art performance on most tested Arabic NLP tasks.


Author(s) Name:  Wissam Antoun, Fady Baly, Hazem Hajj

Journal name:  Computer Science

Conferrence name:  

Publisher name:  arXiv:2003.00104

DOI:  10.48550/arXiv.2003.00104

Volume Information: