Papers by Hanada Taha
A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment (2025.findings-acl)
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| Challenge: | Texts above a student's readability level can lead to disengagement and disengagement . Developing readability models is crucial for improving literacy, language learning, and academic performance. |
| Approach: | They introduce the Balanced Arabic Readability Evaluation Corpus (BAREC) a large-scale, fine-grained dataset for Arabic readability assessment. |
| Outcome: | The proposed model outperforms existing methods in Arabic readability assessment. |
BAREC Demo: Resources and Tools for Sentence-level Arabic Readability Assessment (2025.emnlp-demos)
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| Challenge: | Existing efforts to assess the readability of Arabic text are limited due to its rich morphology, complex syntax, and ambiguous orthography. |
| Approach: | They propose a web-based system for fine-grained, sentence-level Arabic readability assessment. |
| Outcome: | The demo provides two main functionalities for educators, content creators, language learners, and researchers: (1) a Search interface to explore the annotated dataset for text selection and resource development; (2) an Analyze interface to assign detailed readability labels to Arabic texts at the sentence level. |