"Sharks are not the threat humans are": Argument Component Segmentation in School Student Essays
This work addresses argument mining in educational contexts, specifically for analyzing student essays, but it is incremental as it applies existing methods to a new dataset.
The researchers tackled the problem of segmenting argumentative units in middle school student essays by applying token-level classification to identify claim and premise tokens, and they found that a BERT-based multi-task learning architecture, adaptively pretrained on relevant unlabeled data, achieved the best results.
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