Argument Mining

89 papers with code • 1 benchmarks • 7 datasets

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

Most implemented papers

Categorizing Comparative Sentences

uhh-lt/comparative WS 2019

We tackle the tasks of automatically identifying comparative sentences and categorizing the intended preference (e. g., "Python has better NLP libraries than MATLAB" => (Python, better, MATLAB).

DebateSum: A large-scale argument mining and summarization dataset

Hellisotherpeople/DebateSum COLING (ArgMining) 2020

Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today.

Where is Your Evidence: Improving Fact-checking by Justification Modeling

Tariq60/LIAR-PLUS WS 2018

Fact-checking is a journalistic practice that compares a claim made publicly against trusted sources of facts.

Classification and Clustering of Arguments with Contextualized Word Embeddings

UKPLab/acl2019-BERT-argument-classification-and-clustering ACL 2019

We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search.

Yes, we can! Mining Arguments in 50 Years of US Presidential Campaign Debates

ElecDeb60To16/Dataset ACL 2019

We address this task in an empirical manner by annotating 39 political debates from the last 50 years of US presidential campaigns, creating a new corpus of 29k argument components, labeled as premises and claims.

Argument Mining Driven Analysis of Peer-Reviews

fromm-m/aaai2021-am-peer-reviews 10 Dec 2020

Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.

ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

serenayj/ABCD-ACL2021 ACL 2021

On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline.

Assessing Convincingness of Arguments in Online Debates with Limited Number of Features

lisanka93/individualProject EACL 2017

We propose a new method in the field of argument analysis in social media to determining convincingness of arguments in online debates, following previous research by Habernal and Gurevych (2016).

Argument Mining with Structured SVMs and RNNs

vene/marseille ACL 2017

We propose a novel factor graph model for argument mining, designed for settings in which the argumentative relations in a document do not necessarily form a tree structure.