Moriah Ariely


2023

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Transformer-based Hebrew NLP models for Short Answer Scoring in Biology
Abigail Gurin Schleifer | Beata Beigman Klebanov | Moriah Ariely | Giora Alexandron
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

Pre-trained large language models (PLMs) are adaptable to a wide range of downstream tasks by fine-tuning their rich contextual embeddings to the task, often without requiring much task-specific data. In this paper, we explore the use of a recently developed Hebrew PLM aleph-BERT for automated short answer grading of high school biology items. We show that the alephBERT-based system outperforms a strong CNN-based baseline, and that it general-izes unexpectedly well in a zero-shot paradigm to items on an unseen topic that address the same underlying biological concepts, opening up the possibility of automatically assessing new items without item-specific fine-tuning.