3 citations · 4 across the 7 of their papers we have counts for
7 papers
ArabiGEE: A Hierarchical Taxonomy for Arabic Grammatical Error Explanation
Khaled Elhady, Omar Kallas, Nizar Habash +1
We introduce ArabiGEE, the first comprehensive Arabic grammatical error explanation (GEE) taxonomy grounded in explicit error types. Unlike existing GEE approaches that treat expla…
A Tale of Two Scripts: Transliteration and Post-Correction for Judeo-Arabic
Juan Moreno Gonzalez, Bashar Alhafni, Nizar Habash
Judeo-Arabic refers to Arabic variants historically spoken by Jewish communities across the Arab world, primarily during the Middle Ages. Unlike standard Arabic, it is written in H…
Enhancing Arabic Automated Essay Scoring with Synthetic Data and Error Injection
Chatrine Qwaider, Bashar Alhafni, Kirill Chirkunov +2
Automated Essay Scoring (AES) plays a crucial role in assessing language learners' writing quality, reducing grading workload, and providing real-time feedback. The lack of annotat…
LLMs in Education: Novel Perspectives, Challenges, and Opportunities
Bashar Alhafni, Sowmya Vajjala, Stefano Bannò +2
The role of large language models (LLMs) in education is an increasing area of interest today, considering the new opportunities they offer for teaching, learning, and assessment.…
The SAMER Arabic Text Simplification Corpus
Bashar Alhafni, Reem Hazim, Juan Piñeros Liberato +2
We present the SAMER Corpus, the first manually annotated Arabic parallel corpus for text simplification targeting school-aged learners. Our corpus comprises texts of 159K words se…
Personalized Text Generation with Fine-Grained Linguistic Control
Bashar Alhafni, Vivek Kulkarni, Dhruv Kumar +1
As the text generation capabilities of large language models become increasingly prominent, recent studies have focused on controlling particular aspects of the generated text to m…
mEdIT: Multilingual Text Editing via Instruction Tuning
Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni +2
We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent state-of-the-art text editing models for writing assistance. mEdIT models are trained by fine-tuning multi-lin…