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About this paper
Xia Zeng, Amani S. Abumansour, Arkaitz Zubiaga
This survey paper by researchers from Queen Mary University of London provides a comprehensive review of automated fact-checking, focusing on the pipelines of claim detection and claim validation. It systematically analyzes current methodologies, available datasets, and identifies prevailing challenges and future research directions in the field.
Key insights
- Automated fact-checking systems are typically conceptualized as pipelines, with claim detection and validation as core stages, but these pipelines can suffer from error accumulation.
- Pre-trained language models are increasingly dominant for claim verification due to their effectiveness in Natural Language Inference (NLI) tasks.
- Significant challenges persist in defining 'check-worthiness,' developing high-quality and balanced datasets across diverse domains, and ensuring model interpretability.
Summary by alphaXiv.
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