Papers by May Bashendy
MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring (2026.findings-acl)
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| Challenge: | Current approaches to automate essay scoring (AES) treat each writing task as a separate task, resulting in inconsistent performance. |
| Approach: | They propose a meta-learning framework that leverages prototypical networks to learn transferable representations across different writing prompts. |
| Outcome: | The proposed framework outperforms baseline models on ELLIPSE and ASAP (English) and LAILA (Arabic) on three diverse datasets. |
LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring (2026.eacl-long)
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May Bashendy, Walid Massoud, Sohaila Eltanbouly, Salam Albatarni, Marwan Sayed, Abrar Abir, Houda Bouamor, Tamer Elsayed
| Challenge: | Existing Arabic resources are small in scale and lack trait-specific annotations. |
| Approach: | They propose to use LAILA to build a large Arabic AES dataset with holistic and trait-specific annotations of seven writing proficiency traits. |
| Outcome: | The LAILA dataset comprises 7,859 essays annotated with holistic and trait-specific scores on seven dimensions: relevance, organization, vocabulary, style, development, mechanics, and grammar. |