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New dataset aids enthymeme detection in 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.

RANK_REASON The cluster contains an academic paper detailing a new dataset and methodology for NLP research.

Read on arXiv cs.CL →

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Martial Pastor, Nelleke Oostdijk ·

    A Resource for Enthymeme Detection in Controversial Political Discourse

    arXiv:2606.12186v1 Announce Type: new Abstract: Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective. We present a resource of 1,482 tweets from politically controversial discourse,…

  2. arXiv cs.CL TIER_1 English(EN) · Nelleke Oostdijk ·

    A Resource for Enthymeme Detection in Controversial Political Discourse

    Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective. We present a resource of 1,482 tweets from politically controversial discourse, annotated by five annotators for the presence o…