AraBERT: Transformer-based Model for Arabic Language Understanding

TLDR

Arabic is a morphologically rich language with limited resources, making NLP tasks such as sentiment analysis, named entity recognition, and question answering challenging, though transformer‑based BERT models have proven effective when pre‑trained on large corpora. This work pre‑trains a BERT model specifically for Arabic to replicate the success achieved by BERT on English. AraBERT is trained on a large Arabic corpus and its performance is benchmarked against multilingual BERT and other state‑of‑the‑art models on standard Arabic NLP tasks. AraBERT achieves state‑of‑the‑art results on most Arabic NLP tasks, and the pretrained models are released on GitHub to support further research.

Abstract

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. The pretrained araBERT models are publicly available on https://github.com/aub-mind/arabert hoping to encourage research and applications for Arabic NLP.