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Azzah Allahim

As seen in: Nature, MDPI
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Articles

Advancing Arabic automated essay scoring through cross-encoder BERT models and interpretable explanations

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By Ayman Mohamed Mostafa, Meshrif Alruily, Hisham Allahem, Azzah Allahim, Abdulaziz Shehab, Menwa Alshammeri, Mohammed Alnusayri, Mohamed Ezz
| Nature Verified
Abstract Automated Essay Scoring (AES) in Arabic remains underdeveloped due to the language’s diglossia, rich morphology, and limited annotated resources, reflecting inherent linguistic asymmetry. This study advances Arabic AES by introducing a BERT-based sentence-pair cross-encoder framework that leverages the structural symmetry of bidirectional attention and combines it with an Integrated Gradients (IG) explanation pipeline.
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Advancing Arabic Word Embeddings: A Multi-Corpora Approach with Optimized Hyperparameters and Custom Evaluation

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By Azzah Allahim, Asma Cherif
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Advancing Arabic Word Embeddings: A Multi-Corpora Approach with Optimized Hyperparameters and Custom Evaluation

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By Asma Cherif, Azzah Allahim
| Preprints.org
Preprint Article Version 1 This version is not peer-reviewed Version 1 : Received: 15 October 2024 / Approved: 16 October 2024 / Online: 17 October 2024 (02:44:46 CEST) Allahim, A.; Cherif, A. Advancing Arabic Word Embeddings: A Multi-Corpora Approach with Optimized Hyperparameters and Custom Evaluation. Preprints 2024, 2024101288. https://doi.org/10.20944/preprints202410.1288.v1 Allahim, A.; Cherif, A.
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