@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.",
}
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<title>Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)</title>
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<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.</abstract>
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%0 Conference Proceedings
%T Transformer-based Hebrew NLP models for Short Answer Scoring in Biology
%A Gurin Schleifer, Abigail
%A Beigman Klebanov, Beata
%A Ariely, Moriah
%A Alexandron, Giora
%Y Kochmar, Ekaterina
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Madnani, Nitin
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%Y Zesch, Torsten
%S Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F gurin-schleifer-etal-2023-transformer
%X 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.
%R 10.18653/v1/2023.bea-1.46
%U https://aclanthology.org/2023.bea-1.46
%U https://doi.org/10.18653/v1/2023.bea-1.46
%P 550-555
Markdown (Informal)
[Transformer-based Hebrew NLP models for Short Answer Scoring in Biology](https://aclanthology.org/2023.bea-1.46) (Gurin Schleifer et al., BEA 2023)
ACL