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 101–110 of 284 papers

TitleStatusHype
Investigating Logic Tensor Networks for Neural-Symbolic Argument Mining—0
Data-Driven Detection of General Chiasmi Using Lexical and Semantic FeaturesCode0
Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate—0
Multi-task Learning in Argument Mining for Persuasive Online Discussions—0
Self-trained Pretrained Language Models for Evidence Detection—0
M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and TranscriptsCode1
Citizen Involvement in Urban Planning - How Can Municipalities Be Supported in Evaluating Public Participation Processes for Mobility Transitions?Code0
Image Retrieval for Arguments Using Stance-Aware Query Expansion—0
Overview of the 2021 Key Point Analysis Shared Task—0
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language ModelsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified