FEVER: A large-scale dataset for fact extraction and verification
- Submitting institution
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University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1938
- Type
- E - Conference contribution
- DOI
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10.18653/v1/n18-1074
- Title of conference / published proceedings
- NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference
- First page
- 809
- Volume
- 1
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- January
- Year of publication
- 2018
- URL
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-
- Supplementary information
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-
- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The dataset presented in this paper is the largest resource for developing automated verification systems. It was used to organize two shared tasks with 30 participating teams, and the workshops presenting the results attracted researchers from academia, industry and journalism. It is often used to evaluate the state of the art in machine reading and text understanding at scale. The collaboration with Amazon has continued resulting a fully funded PhD studentship and the work led to Vlachos being awarded an ERC consolidator award of 2 million Euros to continue his work on automated fact checking.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -