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Authors: Hadi Abdi Ghavidel ; Amal Zouaq and Michel C. Desmarais

Affiliation: Department of Computer Engineering and Software Engineering, Polytechnique Montréal, Montreal, Canada

Keyword(s): Automatic Short Answer Grading, SciEntBank, BERT, XLNET.

Abstract: Over the last decade, there has been a considerable amount of research in automatic short answer grading (ASAG). The majority of previous experiments were based on a feature engineering approach and used manually-engineered statistical, lexical, grammatical and semantic features for ASAG. In this study, we aim for an approach that is free from manually-engineered features and propose an architecture for deep learning based on the newly-introduced BERT (Bidirectional Encoder Representations from Transformers) and XLNET (Extra Long Network) classifiers. We report the results achieved over one of the most popular dataset for ASAG, SciEntBank. Compared to past works for the SemEval-2013 2-way, 3-way and 5-way tasks, we obtained better or competitive performance with BERT Base (cased and uncased) and XLNET Base (cased) using a reference-based approach (considering students and model answers) and without any type of hand-crafted features.

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Paper citation in several formats:
Ghavidel, H. ; Zouaq, A. and Desmarais, M. (2020). Using BERT and XLNET for the Automatic Short Answer Grading Task. In Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU; ISBN 978-989-758-417-6; ISSN 2184-5026, SciTePress, pages 58-67. DOI: 10.5220/0009422400580067

@conference{csedu20,
author={Hadi Abdi Ghavidel and Amal Zouaq and Michel C. Desmarais},
title={Using BERT and XLNET for the Automatic Short Answer Grading Task},
booktitle={Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU},
year={2020},
pages={58-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009422400580067},
isbn={978-989-758-417-6},
issn={2184-5026},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Computer Supported Education - Volume 1: CSEDU
TI - Using BERT and XLNET for the Automatic Short Answer Grading Task
SN - 978-989-758-417-6
IS - 2184-5026
AU - Ghavidel, H.
AU - Zouaq, A.
AU - Desmarais, M.
PY - 2020
SP - 58
EP - 67
DO - 10.5220/0009422400580067
PB - SciTePress

- Science and Technology Publications, Lda.
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