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 1–10 of 284 papers

TitleStatusHype
LawInstruct: A Resource for Studying Language Model Adaptation to the Legal DomainCode1
Exploring the Potential of Large Language Models in Computational ArgumentationCode1
Detecting Arguments in CJEU Decisions on Fiscal State AidCode1
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument MiningCode1
Mining Legal Arguments in Court DecisionsCode1
Identifying the Human Values behind ArgumentsCode1
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining TasksCode1
M-Arg: Multimodal Argument Mining Dataset for Political Debates with Audio and TranscriptsCode1
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language ModelsCode1
ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple SentencesCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified