⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

January 1, 2021Open Access

ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic

Muhammad Abdul-Mageed AbdelRahim Elmadany El Moatez Billah Nagoudi

View Full Paper

Ask AI

Bookmark

Share

Key Points

This research aims to create and evaluate two deep bidirectional transformer models, ARBERT and MARBERT, for Arabic natural language processing.
Developed ARBERT and MARBERT models based on transformer architecture.
Utilized Arabic textual data for training and evaluation.
Compared performance against existing language models for Arabic.
ARBERT achieved improved performance over current models on Arabic NLP tasks.
MARBERT demonstrated enhanced contextual understanding of Arabic text in various applications.

Implication

Research presents two deep learning models for Arabic language processing, suggesting advancements in NLP applications.

Abstract

No abstract yet. The journal hasn't published one we can show.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Abdul-Mageed et al. (2021) studied this question.

synapsesocial.com/papers/6a01c72b1adb974501caf7a9 https://doi.org/10.18653/v1/2021.acl-long.551

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Maghrebi dialects – Arabic bidirectional translation: an improved transformer with transfer learningOriginal Research · 2026 · 1 citations
2ArabBert-LSTM: improving Arabic sentiment analysis based on transformer model and Long Short-Term Memory2024 · 29 citations
3MagBERT: A Compact Multi-Dialectal BERT for Low-Resource Maghrebi ArabicOriginal Research · 2026
4A Comparative Study of Deep Learning Approaches for Arabic Language Processing2024 · 2 citations
5Using Transformers to Classify Arabic Dialects on Social Networks2024 · 4 citations

A Transformer-Based Approach for Multimodal Arabic Sentiment Analysis2026
Arabic Dialect Identification in Social Media: A Hybrid Model with Transformer Models and BiLSTM2024
BERT-LA: Leveraging BERT and AraBERT With Bi-LSTM for Cross-Lingual Sentiment Analysis of English and Arabic Texts2024

More from University of British Columbia