no code implementations • EMNLP (FEVER) 2021 • Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal
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).
no code implementations • ArgMining (ACL) 2022 • Mehmet Sofi, Matteo Fortier, Oana Cocarascu
Additionally, we integrate existing robustness tests designed for other natural language processing tasks and re-purpose them for argument mining.
1 code implementation • Findings (NAACL) 2022 • Mubashara Akhtar, Oana Cocarascu, Elena Simperl
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.
1 code implementation • 13 Nov 2023 • Mubashara Akhtar, Nikesh Subedi, Vivek Gupta, Sahar Tahmasebi, Oana Cocarascu, Elena Simperl
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.
no code implementations • 3 Nov 2023 • Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, Arpit Patil, Oana Cocarascu, Elena Simperl
Thus, understanding and reasoning with numbers are essential skills for language models to solve different tasks.
no code implementations • 25 Oct 2023 • Madeleine Waller, Odinaldo Rodrigues, Oana Cocarascu
As algorithmic decision-making systems become more prevalent in society, ensuring the fairness of these systems is becoming increasingly important.
no code implementations • 31 May 2023 • Madeleine Waller, Odinaldo Rodrigues, Oana Cocarascu
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.
1 code implementation • 22 May 2023 • Mubashara Akhtar, Michael Schlichtkrull, Zhijiang Guo, Oana Cocarascu, Elena Simperl, Andreas Vlachos
In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation.
no code implementations • 27 Jan 2023 • Mubashara Akhtar, Oana Cocarascu, Elena Simperl
Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text, tables, images, audio, or video.
1 code implementation • 10 Jun 2021 • Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal
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.
no code implementations • 28 May 2021 • Oana Cocarascu, Andrew McLean, Paul French, Francesca Toni
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.
no code implementations • 23 May 2021 • Joel Oksanen, Oana Cocarascu, Francesca Toni
Ontologies have proven beneficial in different settings that make use of textual reviews.
no code implementations • 14 Feb 2020 • Oana Cocarascu, Elena Cabrio, Serena Villata, Francesca Toni
Argument Mining is the research area which aims at extracting argument components and predicting argumentative relations (i. e., support and attack) from text.
no code implementations • WS 2019 • James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
We present the results of the second Fact Extraction and VERification (FEVER2. 0) Shared Task.
no code implementations • CL 2018 • Oana Cocarascu, Francesca Toni
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.
no code implementations • WS 2018 • James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
We present the results of the first Fact Extraction and VERification (FEVER) Shared Task.
no code implementations • EMNLP 2017 • Oana Cocarascu, Francesca Toni
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.