@inproceedings{gurin-schleifer-etal-2023-transformer,
    title = "Transformer-based {H}ebrew {NLP} models for Short Answer Scoring in Biology",
    author = "Gurin Schleifer, Abigail  and
      Beigman Klebanov, Beata  and
      Ariely, Moriah  and
      Alexandron, Giora",
    editor = {Kochmar, Ekaterina  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Madnani, Nitin  and
      Tack, Ana{\"\i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng  and
      Zesch, Torsten},
    booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bea-1.46",
    doi = "10.18653/v1/2023.bea-1.46",
    pages = "550--555",
    abstract = "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.",
}

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.
Gurin Schleifer, Abigail Beigman Klebanov, Beata Ariely, Moriah Alex ron, Giora
Transformer-based Hebrew NLP models for Short Answer Scoring in Biology