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• W3135354815 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" @default.
• W3135354815 created "2021-03-15" @default.
• W3135354815 creator A5047213662 @default.
• W3135354815 creator A5068587810 @default.
• W3135354815 date "2021-03-08" @default.
• W3135354815 modified "2023-09-27" @default.
• W3135354815 title "Sharks are not the threat humans are: Argument Component Segmentation in School Student Essays" @default.
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