AraBERT: Transformer-based Model for Arabic Language Understanding

Understand

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.