Papers by Nada Almarwani
Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset (2025.findings-emnlp)
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Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki, Omer Nacar, Youssef Nafea, Safaa Taher Abdelfadil, Abdulfattah Mohammed Yahya, Hamzah Luqman, Nada Almarwani, Samah Aloufi, Baraah Qawasmeh, Houdaifa Atou, Serry Sibaee, Hamzah A. Alsayadi, Walid Al-Dhabyani, Maged S. Al-shaibani, Aya El aatar, Nour Qandos, Rahaf Alhamouri, Samar Ahmad, Mohammed Anwar AL-Ghrawi, Aminetou Yacoub, Ruwa AbuHweidi, Vatimetou Mohamed Lemin, Reem Abdel-Salam, Ahlam Bashiti, Adel Ammar, Aisha Alansari, Ahmed Ashraf, Nora Alturayeif, Alcides Alcoba Inciarte, AbdelRahim A. Elmadany, Mohamedou Cheikh Tourad, Ismail Berrada, Mustafa Jarrar, Shady Shehata, Muhammad Abdul-Mageed
| Challenge: | Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. |
| Approach: | They propose to construct a large-scale Arabic multimodal dataset and benchmark explicitly designed for cultural understanding. |
| Outcome: | The proposed dataset covers ten culturally significant domains covering all Arab countries and includes two evaluation benchmarks (PEARL and PEARL-LITE) and a specialized subset (PearL-X). |
Efficient Sentence Embedding using Discrete Cosine Transform (D19-1)
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| Challenge: | Modern NLP systems rely on word embeddings as input units to encode statistical semantic and syntactic properties of words. |
| Approach: | They propose to use discrete cosine transform to compress word sequences in order-preserving manner. |
| Outcome: | The proposed model preserves syntactic information in semantic probing tasks . it is comparable to vector averaging but mediocre in performance. |
Discrete Cosine Transform as Universal Sentence Encoder (2021.acl-short)
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| Challenge: | Modern sentence encoders capture underlying linguistic characteristics of words . Discrete Cosine Transform (DCT) is an efficient alternative to averaging . |
| Approach: | They propose to use a Discrete Cosine Transform to generate universal sentence representations in different languages. |
| Outcome: | The proposed model captures the underlying syntactic characteristics of a given text without compromising practical efficiency. |