“Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays

Tariq Alhindi, Debanjan Ghosh


Abstract
Argument mining is often addressed by a pipeline method where segmentation of text into argumentative units is conducted first and proceeded by an argument component identification task. In this research, we apply a token-level classification to identify claim and premise tokens from a new corpus of argumentative essays written by middle school students. To this end, we compare a variety of state-of-the-art models such as discrete features and deep learning architectures (e.g., BiLSTM networks and BERT-based architectures) to identify the argument components. We demonstrate that a BERT-based multi-task learning architecture (i.e., token and sentence level classification) adaptively pretrained on a relevant unlabeled dataset obtains the best results.
Anthology ID:
2021.bea-1.22
Volume:
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
April
Year:
2021
Address:
Online
Venues:
BEA | EACL
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
210–222
Language:
URL:
https://aclanthology.org/2021.bea-1.22
DOI:
Bibkey:
Cite (ACL):
Tariq Alhindi and Debanjan Ghosh. 2021. “Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 210–222, Online. Association for Computational Linguistics.
Cite (Informal):
“Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays (Alhindi & Ghosh, BEA 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.bea-1.22.pdf