aiqbench analysis
This research introduces a new dataset of 1,482 annotated political tweets designed to study the subjectivity inherent in detecting enthymemes—arguments with unstated premises. By leveraging annotator disagreement rather than enforcing majority-vote labels, the authors demonstrate that models trained on this variation achieve superior performance in capturing complex human inference processes. The work advances NLP capabilities for analyzing persuasive discourse and highlights the cognitive complexities involved in structuring argumentation schemes for machine learning.
A Resource for Enthymeme Detection in Controversial Political Discourse
arXiv:2606.12186v2 Announce Type: replace 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, annotated by five annotators for the presence of enthymemes and their argument structure, designed to study label variation. We first revisit the definition of enthymemes and propose annotation guidelines anchored in Walton's argumentation schemes, offering a structured and constrained approach that nonetheless preserves room for the interpretive nature of the task. This contrasts with past resources, which tend to eliminate disagreement, obscuring its sources and preventing investigation of its potential benefits for model performance. We further propose a complexity analysis of the task, identifying where annotation imposes high cognitive load and may give rise to inconsistent annotation. Our preliminary experiments show that models trained on annotator disagreement outperform models trained on hard majority-vote labels. We close by reflecting on how structural openness in enthymeme definitions and guidelines enables the study of variation in subjective inferential processes for future resources and downstream NLP applications concerned with human inference.
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, annotated by five annotators for the presence of enthymemes and their argument structure, designed to study label variation. We first revisit the definition of enthymemes and propose annotation guidelines anchored in Walton's argumentation schemes, offering a structured and constrained approach that nonetheless preserves room for the interpretive nature of the task. This contrasts with past resources, which tend to eliminate disagreement, obscuring its sources and preventing investigation of its potential benefits for model performance. We further propose a complexity analysis of the task, identifying where annotation imposes high cognitive load and may give rise to inconsistent annotation. Our preliminary experiments show that models trained on annotator disagreement outperform models trained on hard majority-vote labels. We close by reflecting on how structural openness in enthymeme definitions and guidelines enables the study of variation in subjective inferential processes for future resources and downstream NLP applications concerned with human inference.
Computation and Language (cs.CL)
arXiv:2606.12186 [cs.CL]
(or arXiv:2606.12186v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.12186 Focus to learn more arXiv-issued DOI via DataCite
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