Argument Mining
85 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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UKP
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Subtasks
Latest papers with no code
Are Large Language Models Reliable Argument Quality Annotators?
In this paper, we study the potential of using state-of-the-art large language models (LLMs) as proxies for argument quality annotators.
Mind Your Neighbours: Leveraging Analogous Instances for Rhetorical Role Labeling for Legal Documents
Rhetorical Role Labeling (RRL) of legal judgments is essential for various tasks, such as case summarization, semantic search and argument mining.
Efficient argument classification with compact language models and ChatGPT-4 refinements
Argument mining (AM) is defined as the task of automatically identifying and extracting argumentative components (e. g. premises, claims, etc.)
A Hybrid Intelligence Method for Argument Mining
We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-the-art automated method when compared to a common set of diverse opinions, justifying the need for human insight.
NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes
Some of the major limitations identified in the areas of argument mining, argument generation, and natural language argument analysis are related to the complexity of annotating argumentatively rich data, the limited size of these corpora, and the constraints that represent the different languages and domains in which these data is annotated.
Can Large Language Models perform Relation-based Argument Mining?
Argument mining (AM) is the process of automatically extracting arguments, their components and/or relations amongst arguments and components from text.
Automatic Analysis of Substantiation in Scientific Peer Reviews
With the increasing amount of problematic peer reviews in top AI conferences, the community is urgently in need of automatic quality control measures.
Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning
To advance argumentative stance prediction as a multimodal problem, the First Shared Task in Multimodal Argument Mining hosted stance prediction in crucial social topics of gun control and abortion.
AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining
Argument mining is to analyze argument structure and extract important argument information from unstructured text.
How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study
Our findings challenge the previously asserted general superiority of in-context learning (ICL) for OOD.
