A Resource for Enthymeme Detection in Controversial Political Discourse

2026-06-10 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

Martial Pastor and Nelleke Oostdijk introduce a new resource for enthymeme detection, comprising 1,482 tweets from politically controversial discourse. This dataset is uniquely annotated by five annotators for enthymeme presence and argument structure, specifically designed to investigate label variation rather than eliminate disagreement. The authors revisit enthymeme definitions, proposing annotation guidelines anchored in Walton's argumentation schemes to provide a structured yet interpretive approach. They also conduct a complexity analysis of the annotation task to identify sources of inconsistency. Preliminary experiments demonstrate that models trained on this annotator disagreement data achieve superior performance compared to models trained on traditional hard majority-vote labels, highlighting the value of capturing subjective inferential processes.

Key takeaway

For NLP engineers developing argument mining systems, consider designing annotation tasks that explicitly capture and utilize annotator disagreement. Training models on this varied human inference data, rather than just majority-vote labels, can significantly improve performance in detecting complex linguistic phenomena like enthymemes. You should explore structured annotation guidelines, such as those based on Walton's argumentation schemes, to manage subjectivity while preserving valuable interpretive nuances for model training.

Key insights

The study of annotator disagreement in enthymeme detection improves model performance and understanding of human inference.

Principles

• Annotation guidelines can be structured yet interpretive.
• Disagreement in subjective tasks offers valuable training data.
• Open definitions enable studying inferential variation.

Method

The authors propose annotation guidelines for enthymemes based on Walton's argumentation schemes, applied to 1,482 tweets, and analyze task complexity.

In practice

• Train NLP models on annotator disagreement.
• Design annotation tasks to capture subjective variation.
• Use Walton's schemes for argument structure.

Topics

• Enthymeme Detection
• Argument Mining
• Annotation Disagreement
• Political Discourse
• NLP Datasets
• Walton's Argumentation Schemes

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.