EthiX: A Dataset for Argument Scheme Classification in Ethical Debates

Elfia Bezou-Vrakatseli, Oana Cocarascu, Sanjay Modgil

Abstract: Argument schemes represent stereotypical patterns of reasoning that capture the inferences from premise(s) to conclusion. Despite their usefulness in argument mining, argument scheme classification remains a largely understudied task in NLP. In this paper, we present EthiX, a novel dataset for classifying argument schemes, comprising arguments spanning 22 ethical topics which are manually annotated with argument schemes following Walton’s taxonomy. We evaluate pre-trained models fine-tuned on our dataset and propose a baseline to the community.