A Resource for Enthymeme Detection in Controversial Political Discourse
Researchers have developed a new dataset of 1,482 tweets from controversial political discussions to study enthymeme detection. Enthymemes, arguments with unstated premises, are difficult to annotate due to subjectivity. The dataset, annotated by five individuals, aims to capture label variation and explore its impact on model performance. Preliminary experiments suggest that models trained on annotator disagreement yield better results than those using majority-vote labels. AI
IMPACT Provides a novel dataset and approach for training NLP models to understand nuanced arguments in political discourse.