Abstract
Argument mining is the process of automatic extraction of certain argu- mentation structures from data. Argument mining consists of several stages such as argument component detection, argument component clas- sification, and argumentative discourse analysis. The lack of training data in low resource languages is a common issue in argument mining applica- tions. In this work we analyse the possibilities for the application of zero- shot and few-shot language transfer models trained on the language ma- terial in a resource-rich language (English) for the tasks of argument com- ponent detection, and argument component classification in a low-resource language (Norwegian) with the aim to find out if these techniques can help overcome the challenge of no available training data. In addition, we com- pare models based on different transformer architectures and experiment with additional hand-crafted features.