  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
