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AraBERT: Transformer-based Model for Arabic Language Understanding

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arxiv 2003.00104 v4 pith:UVBHJJSK submitted 2020-02-28 cs.CL

Wissam Antoun , Fady Baly , Hazem Hajj This is my paper
classification cs.CL
keywords languagearabicarabertbertmodelsstate-of-the-arttasksvery
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 619 citations worldwide. Full citation record

  1. Fann or Flop: A Multigenre, Multiera Benchmark for Arabic Poetry Understanding in LLMs

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    The new Fann or Flop benchmark measures LLM comprehension of Arabic poetry through expert-written verse explanations and shows current LLMs perform poorly on interpretive depth.

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  3. Evaluation of Adversarial Robustness in Arabic Language Models

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    Arabic BERT-family sentiment models lose up to 92% accuracy under diacritics and 58% under conjunction attacks; paraphrase attacks cut accuracy by 76% on average, and adversarial training only partially helps.

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    The paper reports state-of-the-art WERs of 13.07% (signer-independent) and 47.78% (unseen sentences) on Isharah-1000 using a conformer and a multi-scale fusion transformer.

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    For Arabic retrieval-augmented generation, sentence-aware chunking, BGE-M3 and Multilingual-E5-large embeddings, bge-reranker-v2-m3, and Aya-8B yield the highest RAGAS scores across six Arabic datasets.

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  10. GATE: General Arabic Text Embedding for Enhanced Semantic Textual Similarity with Matryoshka Representation Learning and Hybrid Loss Training

    cs.CL 2025-05 reject novelty 4.0 of 10

    GATE's Arabic-Triplet-Matryoshka-V2 reports the highest average scores on the MTEB Arabic STS17/STS22/STS22-v2 tasks among the models compared in the paper.

  11. DS@GT ARC at CheckThat! 2026: LLM-Based Trace Ranking and Grouped Reward Modeling for Multilingual Numerical Claim Verification

    cs.CL 2026-07 conditional novelty 3.5 of 10

    LoRA-tuned LLM trace scoring beats a TF-IDF grouped reward model on most English metrics and Recall@5, AraBERT beats multilingual BERT on Arabic, and prompt-based sub-claim decomposition hurts rather than helps.

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