Abstract
The research of arguments in student essays has long been the subject of automatic approaches to argument mining. The task has been mostly modeled as a sequence tagging problem, where the text is either analyzed in its entirety or split into smaller homogeneous units, such as sentences or paragraphs. However, previous research has highlighted how the various essay sections may fulfill different functions, and thereby how the position of specific argument components obeys precise structural dependency criteria. Based on such underpinning we propose an approach that exploits such structural information: in this work we present a hybrid training approach that takes into account the specific structural components of the essays, in order to be able to mine different types of argument components at different levels. Our hybrid approach achieves an improvement over essay-level and paragraph-level training, in particular in the extraction of some specific argument components.
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Notes
- 1.
Experiments were performed on machinery provided by the Competence Centre for Scientific Computing [2]; nodes employed were equipped with 8VCPU, 1x NVIDIA Tesla T4 GPU and 64GB Memory. Running experiments with LONGFORMER took 11.5 h to complete 15 epochs at essay-level, while BERT only took 4 h. At paragraph-level instead, LONGFORMER took 28 h, BERT 9.5.
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Demaria, R., Colla, D., Delsanto, M., Mensa, E., Pasini, E., Radicioni, D.P. (2023). Mining Argument Components in Essays at Different Levels. In: Basili, R., Lembo, D., Limongelli, C., Orlandini, A. (eds) AIxIA 2023 – Advances in Artificial Intelligence. AIxIA 2023. Lecture Notes in Computer Science(), vol 14318. Springer, Cham. https://doi.org/10.1007/978-3-031-47546-7_10
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