Papers by Sultan Alrowili

2 papers
ArabicTransformer: Efficient Large Arabic Language Model with Funnel Transformer and ELECTRA Objective (2021.findings-emnlp)

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Challenge: Existing solutions to reduce the cost of pretraining Transformer-based models are expensive especially for large-scale models.
Approach: They propose to reduce the cost of pre-training Transformer-based models by compressing the sequence of hidden states inside Transformer architecture.
Outcome: The proposed model achieves state-of-the-art on several Arabic downstream tasks despite using less computational resources compared to other BERT-based models.
AraVQA: Building a New Arabic Factoid Visual Question Answering Dataset from Wikipedia (2026.acl-long)

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Challenge: Existing Arabic VQA datasets focus on culturally-specific and dialect-aware domains.
Approach: They propose a pipeline that leverages Wikipedia template tags to extract relevant information for each image and utilize it to generate a new visual question answering dataset.
Outcome: The proposed pipeline can enhance existing VLMs on Arabic VQA tasks by leveraging Wikipedia template tags.

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