Implicit Argument Detection¶
Implicit argument detection refers to the computational task of identifying and reconstructing argumentative components (premises, conclusions, warrants, or evidence) that are not explicitly stated in the text. This is crucial for understanding persuasion, deception, and information integrity because the most effective misleading arguments often leave key claims implicit, relying on readers' prior beliefs or knowledge to fill in the gaps.
The field draws on classical rhetoric (enthymemes, Aristotelian logic), modern argumentation theory (Walton argument schemes), and NLP techniques (embeddings, commonsense reasoning, discourse parsing). Tasks range from binary detection (is an argument present?) to structured recovery (what is the missing premise?).
Key papers¶
- Interpretive Variation and Discourse Structure in Detecting Enthymemes in Controversial Political Discourse — focuses on binary detection and handles inherent disagreement in how readers interpret implicit premises.
Related topics¶
- Enthymeme detection (a specific form of implicit argument)
- Natural Language Inference (reasoning over implicit information)
- Rhetorical analysis (understanding persuasion structure)
- Computational Argumentation (broader field)