Search Results for author: Michael Schlichtkrull

Found 12 papers, 9 papers with code

AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web

1 code implementation NeurIPS 2023 Michael Schlichtkrull, Zhijiang Guo, Andreas Vlachos

Existing datasets for automated fact-checking have substantial limitations, such as relying on artificial claims, lacking annotations for evidence and intermediate reasoning, or including evidence published after the claim.

Claim Verification Fact Checking +1

Multimodal Automated Fact-Checking: A Survey

1 code implementation22 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.

Fact Checking Misinformation

The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who

1 code implementation27 Apr 2023 Michael Schlichtkrull, Nedjma Ousidhoum, Andreas Vlachos

Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation.

Fact Checking Misinformation

A Survey on Automated Fact-Checking

1 code implementation26 Aug 2021 Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem.

Fact Checking Misinformation

FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

1 code implementation10 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.

Fact Verification Misinformation

Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages

1 code implementation EACL 2017 Michael Schlichtkrull, Anders S{\o}gaard

In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding.

Dependency Parsing

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