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    <abstract>Automated Essay Scoring (AES) faces significant challenges in cross-prompt settings, where models must generalize to unseen writing prompts. To address this limitation, we propose MAPLE, a meta-learning framework that leverages prototypical networks to learn transferable representations across different writing prompts. Across three diverse datasets (ELLIPSE and ASAP (English), and LAILA (Arabic)), MAPLE achieves state-of-the-art performance on ELLIPSE and LAILA, outperforming strong baselines by 8.5 and 3 points in QWK, respectively. On ASAP, where prompts exhibit heterogeneous score ranges, MAPLE yields improvements on several traits, highlighting the strengths of our approach in unified scoring settings. Overall, our results demonstrate the potential of meta-learning for building robust cross-prompt AES systems.</abstract>
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%0 Conference Proceedings
%T MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring
%A Albatarni, Salam
%A Bashendy, May
%A Eltanbouly, Sohaila
%A Elsayed, Tamer
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F albatarni-etal-2026-maple
%X Automated Essay Scoring (AES) faces significant challenges in cross-prompt settings, where models must generalize to unseen writing prompts. To address this limitation, we propose MAPLE, a meta-learning framework that leverages prototypical networks to learn transferable representations across different writing prompts. Across three diverse datasets (ELLIPSE and ASAP (English), and LAILA (Arabic)), MAPLE achieves state-of-the-art performance on ELLIPSE and LAILA, outperforming strong baselines by 8.5 and 3 points in QWK, respectively. On ASAP, where prompts exhibit heterogeneous score ranges, MAPLE yields improvements on several traits, highlighting the strengths of our approach in unified scoring settings. Overall, our results demonstrate the potential of meta-learning for building robust cross-prompt AES systems.
%U https://aclanthology.org/2026.findings-acl.997/
%P 19948-19961

Markdown (Informal)

[MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring](https://aclanthology.org/2026.findings-acl.997/) (Albatarni et al., Findings 2026)

• MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring (Albatarni et al., Findings 2026)

ACL

• Salam Albatarni, May Bashendy, Sohaila Eltanbouly, and Tamer Elsayed. 2026. MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19948–19961, San Diego, California, United States. Association for Computational Linguistics.