@inproceedings{alhindi-ghosh-2021-sharks,
title = "{``}Sharks are not the threat humans are{''}: Argument Component Segmentation in School Student Essays",
author = "Alhindi, Tariq and
Ghosh, Debanjan",
booktitle = "Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.bea-1.22",
pages = "210--222",
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.",
}
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%0 Conference Proceedings
%T “Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays
%A Alhindi, Tariq
%A Ghosh, Debanjan
%S Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F alhindi-ghosh-2021-sharks
%X 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.
%U https://aclanthology.org/2021.bea-1.22
%P 210-222
Markdown (Informal)
[“Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays](https://aclanthology.org/2021.bea-1.22) (Alhindi & Ghosh, BEA 2021)
ACL