Corpus ID: 211678011

AraBERT: Transformer-based Model for Arabic Language Understanding

@article{Antoun2020AraBERTTM,
  title={AraBERT: Transformer-based Model for Arabic Language Understanding},
  author={Wissam Antoun and Fady Baly and Hazem M. Hajj},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.00104}
}
  • Wissam Antoun, Fady Baly, Hazem M. Hajj
  • Published 2020
  • Computer Science
  • ArXiv
  • 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… CONTINUE READING

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