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Argument mining is often addressed by a pipeline method where segmentation oftext into argumentative units is conducted first and proceeded by an argumentcomponent identification task. In this research, we apply a token-levelclassification to identify claim and premise tokens from a new corpus ofargumentative essays written by middle school students. To this end, we comparea variety of state-of-the-art models such as discrete features and deeplearning architectures (e.g., BiLSTM networks and BERT-based architectures) toidentify the argument components. We demonstrate that a BERT-based multi-tasklearning architecture (i.e., token and sentence level classification)adaptively pretrained on a relevant unlabeled dataset obtains the best results

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