From Political Debates to Deliberative Democracy: A Roadmap to Assess Semi-Supervised Argument Mining with DISPUTool

Argument mining is a field in natural language processing that studies the automatic extraction of arguments and the classification of their structure from free text. This is a promising research area with numerous applications, like fact-checking, qualitative assessments of online debates, or analysis of legal documents, but with a set of nontrivial challenges to overcome, the main one being the lack of large language resources for the development of this kind of model. In this work, we propose a research path on a subarea of argument mining that has not been explored as much as some other areas: semi-supervised argument mining. We will explore the adaptation of DISPUTool, a model originally trained on data from the US Presidential Elections Political Debates, to deliberative democracy debates within the ORBIS Project for “augmenting participation, co-creation, trust and transparency in deliberative democracy at all scales”. Our discussion highlights the technical and non-technical challenges of this task and our planned path of action to overcome them.