New Dataset for Detecting Enthymemes in Political Discourse Released
Researchers have created a dataset of 1,482 annotated tweets to study enthymemes—arguments with unstated premises or conclusions—in politically controversial discourse. The resource preserves annotator disagreement rather than forcing consensus, reflecting the inherent subjectivity of identifying these rhetorical structures. The approach shows that machine learning models trained on disagreement data outperform those trained on majority-vote labels, suggesting value in capturing interpretive variation.
A new computational linguistics resource addresses the challenge of detecting enthymemes in political discourse by creating a dataset of 1,482 tweets annotated by five independent annotators. Rather than eliminating disagreement through consensus methods, the researchers preserved label variation to study its sources and potential benefits. The annotation guidelines are anchored in Walton's argumentation schemes, providing structured constraints while acknowledging the interpretive nature of identifying unstated premises and conclusions. Preliminary experiments demonstrate that models trained on the full range of annotator disagreement achieve better performance than those trained on hard majority-vote labels. The work includes complexity analysis identifying where the annotation task imposes high cognitive load, and the authors reflect on how preserving structural openness in definitions enables future research into subjective inferential processes in NLP applications.
What's missing
The paper does not discuss inter-annotator agreement metrics (such as Krippendorff's alpha or Cohen's kappa) or provide details on how disagreement was quantified and analyzed. Additionally, the specific composition of the political discourse (e.g., which controversies or time periods) and the demographic or expertise background of annotators are not described in the abstract.
What different sources said
- arXiv cs.CLCenter
A Resource for Enthymeme Detection in Controversial Political Discourse
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