SOTAVerified

Argument Mining

Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.

Source: AMPERSAND: Argument Mining for PERSuAsive oNline Discussions

Papers

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Showing 41–50 of 284 papers

TitleStatusHype
TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument MiningCode0
"I'd Like to Have an Argument, Please": Argumentative Reasoning in Large Language ModelsCode0
AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining—0
How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field StudyCode0
Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources—0
DebateKG: Automatic Policy Debate Case Creation with Semantic Knowledge GraphsCode0
Multi-Task Learning Improves Performance In Deep Argument Mining Models—0
Cross-Genre Argument Mining: Can Language Models Automatically Fill in Missing Discourse Markers?—0
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative ApproachCode0
Argument Mining using BERT and Self-Attention based Embeddings—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified