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 26–50 of 284 papers

TitleStatusHype
Annotation of argument structure in Japanese legal documents—0
AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments—0
A Preliminary Study of Disputation Behavior in Online Debating Forum—0
Advances in Debating Technologies: Building AI That Can Debate Humans—0
A Corpus of Annotated Revisions for Studying Argumentative Writing—0
Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate—0
A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks—0
Argument Summarization and its Evaluation in the Era of Large Language Models—0
AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining—0
A Cascade Model for Proposition Extraction in Argumentation—0
An Empirical Study on Measuring the Similarity of Sentential Arguments with Language Model Domain Adaptation—0
TACAM: Topic And Context Aware Argument Mining—0
Argument Mining: A Survey—0
An Argument-Annotated Corpus of Scientific Publications—0
Argument Harvesting Using Chatbots—0
ArgumenText: Searching for Arguments in Heterogeneous Sources—0
Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum—0
Argument Mining: Extracting Arguments from Online Dialogue—0
Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead—0
Argument Mining for Understanding Peer Reviews—0
A News Editorial Corpus for Mining Argumentation Strategies—0
Active Learning for Argument Mining: A Practical Approach—0
Argument Mining on Twitter: Arguments, Facts and Sources—0
Argument Mining: the Bottleneck of Knowledge and Language Resources—0
Argument Mining using BERT and Self-Attention based Embeddings—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified