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 126–150 of 284 papers

TitleStatusHype
Overview of the 2021 Key Point Analysis Shared Task—0
Overview of the CAIL 2023 Argument Mining Track—0
``PageRank'' for Argument Relevance—0
Patterns of Argumentation Strategies across Topics—0
Predicting the Presence of Reasoning Markers in Argumentative Text—0
Proceedings of the 4th Workshop on Argument Mining—0
Proceedings of the 5th Workshop on Argument Mining—0
Proceedings of the 6th Workshop on Argument Mining—0
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)—0
Processing Discourse in Dislog on the TextCoop Platform—0
Project Debater APIs: Decomposing the AI Grand Challenge—0
Projection of Argumentative Corpora from Source to Target Languages—0
Proposed Method for Annotation of Scientific Arguments in Terms of Semantic Relations and Argument Schemes—0
QT30: A Corpus of Argument and Conflict in Broadcast Debate—0
QualiAssistant: Extracting Qualia Structures from Texts—0
Quantifying Qualitative Data for Understanding Controversial Issues—0
Recognizing the Absence of Opposing Arguments in Persuasive Essays—0
Re-using an Argument Corpus to Aid in the Curation of Social Media Collections—0
Rhetorical structure and argumentation structure in monologue text—0
RuArg-2022: Argument Mining Evaluation—0
Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model—0
Scrutable Feature Sets for Stance Classification—0
Segmentation of Argumentative Texts with Contextualised Word Representations—0
Self-trained Pretrained Language Models for Evidence Detection—0
Sentiment-Stance-Specificity (SSS) Dataset: Identifying Support-based Entailment among Opinions.—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TACOmacro F185.06—Unverified