Argument Mining
81 papers with code • 1 benchmarks • 6 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
Benchmarks
These leaderboards are used to track progress in Argument Mining
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x-stance
UKP
DebateSum
Subtasks
Latest papers
VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining
In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks.
ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining
meiqiguo/argmining2022-imagearg
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ArgMining (ACL) 2022
The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e. g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective.
ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining
engsalem/arglegalsumm
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COLING 2022
A challenging task when generating summaries of legal documents is the ability to address their argumentative nature.
Mining Legal Arguments in Court Decisions
trusthlt/mining-legal-arguments
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Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field.
A Cascade Model for Argument Mining in Japanese Political Discussions: the QA Lab-PoliInfo-3 Case Study
The rVRAIN team tackled the Budget Argument Mining (BAM) task, consisting of a combination of classification and information retrieval sub-tasks.
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?
jeevesh8/arg_mining
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ACL 2022
Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining.
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc.
Enhancing Legal Argument Mining with Domain Pre-training and Neural Networks
In this paper, we provide a broad study of both classic and contextual embedding models and their performance on practical case law from the European Court of Human Rights (ECHR).
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models
vamsi995/paraphrase-generator
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*SEM (NAACL) 2022
In this paper, we present and implement a multi-dimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs).
Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments.
