%0 Conference Proceedings %T “Sharks are not the threat humans are”: Argument Component Segmentation in School Student Essays %A Alhindi, Tariq %A Ghosh, Debanjan %Y Burstein, Jill %Y Horbach, Andrea %Y Kochmar, Ekaterina %Y Laarmann-Quante, Ronja %Y Leacock, Claudia %Y Madnani, Nitin %Y Pilán, Ildikó %Y Yannakoudakis, Helen %Y Zesch, Torsten %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