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

• URL: http://arxiv.org/abs/2103.04518v1
• Date: Mon, 8 Mar 2021 02:40:07 GMT
• Title: "Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays
• Authors: Tariq Alhindi and Debanjan Ghosh
• Abstract summary: We apply a token-level classification to identify claim and premise tokens from a new corpus of argumentative essays written by middle school students. 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.
• Score: 3.632177840361928
• License: http://creativecommons.org/licenses/by/4.0/
• 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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