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Found 17 papers, 4 papers with code
A Robustness Evaluation Framework for Argument Mining
Additionally, we integrate existing robustness tests designed for other natural language processing tasks and re-purpose them for argument mining.
The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task
The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) shared task, asks participating systems to determine whether human-authored claims are Supported or Refuted based on evidence retrieved from Wikipedia (or NotEnoughInfo if the claim cannot be verified).
PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking
Inspired by human fact checkers, who use different types of evidence (e. g. tables, images, audio) in addition to text, several datasets with tabular evidence data have been released in recent years.
ChartCheck: Explainable Fact-Checking over Real-World Chart Images
Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked.
Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
Thus, understanding and reasoning with numbers are essential skills for language models to solve different tasks.
Identifying Reasons for Bias: An Argumentation-Based Approach
As algorithmic decision-making systems become more prevalent in society, ensuring the fairness of these systems is becoming increasingly important.
Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations
Bias mitigation methods for binary classification decision-making systems have been widely researched due to the ever-growing importance of designing fair machine learning processes that are impartial and do not discriminate against individuals or groups based on protected personal characteristics.
Multimodal Automated Fact-Checking: A Survey
In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation.
Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text, tables, images, audio, or video.
FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation.
An Explanatory Query-Based Framework for Exploring Academic Expertise
The success of research institutions heavily relies upon identifying the right researchers "for the job": researchers may need to identify appropriate collaborators, often from across disciplines; students may need to identify suitable supervisors for projects of their interest; administrators may need to match funding opportunities with relevant researchers, and so on.
Automatic Product Ontology Extraction from Textual Reviews
Ontologies have proven beneficial in different settings that make use of textual reviews.
A Dataset Independent Set of Baselines for Relation Prediction in Argument Mining
Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i. e., support and attack) from text.
The FEVER2.0 Shared Task
We present the results of the second Fact Extraction and VERification (FEVER2. 0) Shared Task.
Combining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets
In this article, we focus on analyzing whether news headlines support tweets and whether reviews are deceptive by analyzing the interaction or the influence that these texts have on the others, thus exploiting contextual information.
The Fact Extraction and VERification (FEVER) Shared Task
We present the results of the first Fact Extraction and VERification (FEVER) Shared Task.
Identifying attack and support argumentative relations using deep learning
We propose a deep learning architecture to capture argumentative relations of attack and support from one piece of text to another, of the kind that naturally occur in a debate.
