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FEVER: a large-scale dataset for Fact Extraction and VERification [article]

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal
2018 arXiv   pre-print
In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification.  ...  To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles.  ...  The authors would like to thank the team of annotators involved in preparing this dataset.  ... 
arXiv:1803.05355v3 fatcat:kkh3lzbgajb7tkenwe5wmr3c34

FEVER: a Large-scale Dataset for Fact Extraction and VERification

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal
2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)  
In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification.  ...  To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles.  ...  The authors would like to thank the team of annotators involved in preparing this dataset.  ... 
doi:10.18653/v1/n18-1074 dblp:conf/naacl/ThorneVCM18 fatcat:zzvh5ptlarfb7eqkzirjxub4pa

FEVER: a Large-scale Dataset for Fact Extraction and VERification [article]

James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal, Apollo-University Of Cambridge Repository, Apollo-University Of Cambridge Repository
2019
In this paper we introduce a new publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification.  ...  To characterize the challenge of the dataset presented, we develop a pipeline approach and compare it to suitably designed oracles.  ...  The authors would like to thank the following people for their advice and suggestions: David Hardcastle, Marie Hanabusa, Timothy Howd, Neil Lawrence, Benjamin Riedel, Craig Saunders and Iris Spik.  ... 
doi:10.17863/cam.40620 fatcat:hbohxhfvvjbsdf65jahlz3nqri

FANG-COVID: A New Large-Scale Benchmark Dataset for Fake News Detection in German

Justus Mattern, Yu Qiao, Elma Kerz, Daniel Wiechmann, Markus Strohmaier
2021 Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)   unpublished
chine learning models for high stakes decisions and FEVER: a large-scale dataset for fact extraction use interpretable models instead. Nature Machine and VERification.  ...  justusmattern/fang-covid 78 Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER) at EMNLP 2021, pages 78–91  ... 
doi:10.18653/v1/2021.fever-1.9 fatcat:6htb7jkiqva25otjz3dqjdbeem

FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information [article]

Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal
2021 arXiv   pre-print
In this paper we introduce a novel dataset and benchmark, Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS), which consists of 87,026 verified claims.  ...  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.  ...  Acknowledgements We would like to thank Amazon for sponsoring the dataset generation and supporting the FEVER workshop and shared task.  ... 
arXiv:2106.05707v3 fatcat:redxetlacrehzosiaze7ufzysu

The Fact Extraction and VERification (FEVER) Shared Task [article]

James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, Arpit Mittal
2018 arXiv   pre-print
We present the results of the first Fact Extraction and VERification (FEVER) Shared Task.  ...  The best performing system achieved a FEVER score of 64.21%.  ...  A.4 Papelo We develop a system for the FEVER fact extraction and verification challenge that uses a high precision entailment classifier based on transformer networks pretrained with language modeling  ... 
arXiv:1811.10971v2 fatcat:gzpfegyi5nbhtk6cqp5hm6lbnq

Team Papelo: Transformer Networks at FEVER [article]

Christopher Malon
2019 arXiv   pre-print
We develop a system for the FEVER fact extraction and verification challenge that uses a high precision entailment classifier based on transformer networks pretrained with language modeling, to classify  ...  In preliminary evaluation, the system achieves .5736 FEVER score, .6108 label accuracy, and .6485 evidence F1 on the FEVER shared task test set.  ...  Introduction The release of the FEVER fact extraction and verification dataset (Thorne et al., 2018) provides a large-scale challenge that tests a combination of retrieval and textual entailment capabilities  ... 
arXiv:1901.02534v1 fatcat:3xlsv7s7bventldtpld3gdpn6e

A Review on Fact Extraction and Verification [article]

Giannis Bekoulis, Christina Papagiannopoulou, Nikos Deligiannis
2021 arXiv   pre-print
We study the fact checking problem, which aims to identify the veracity of a given claim. Specifically, we focus on the task of Fact Extraction and VERification (FEVER) and its accompanied dataset.  ...  structured dataset on the fact extraction and verification task.  ...  A Review on Fact Extraction and Verification 0:7 METHODS In this section, we describe the various methods that have been developed so far for solving the FEVER task.  ... 
arXiv:2010.03001v5 fatcat:zzp6qnqqknhsbowo7x36lopcmm

Connecting the Dots Between Fact Verification and Fake News Detection [article]

Qifei Li, Wangchunshu Zhou
2020 arXiv   pre-print
Fact verification models have enjoyed a fast advancement in the last two years with the development of pre-trained language models like BERT and the release of large scale datasets such as FEVER.  ...  Then we use a fact verification model pre-trained on the FEVER dataset to detect whether the input news article is real or fake.  ...  Acknowledgments We thank the anonymous reviewers for their valuable comments.  ... 
arXiv:2010.05202v1 fatcat:vvjqynhqgzckxofdintt6oqghm

Unsupervised Natural Question Answering with a Small Model [article]

Martin Andrews, Sam Witteveen
2019 arXiv   pre-print
demonstration of the power of huge language models such as GPT-2 to memorise the answers to factoid questions raises questions about the extent to which knowledge is being embedded directly within these large  ...  Acknowledgments The authors would like to thank Google for access to the TFRC TPU program which was used in training and fine-tuning models during experimentation for this paper.  ...  Being able to capture such higher order features provides a natural incentive to want to scale the training of language models to as large a number of parameters as possible.  ... 
arXiv:1911.08340v1 fatcat:crqnwtuulzevpamgfegkvjo7uu

Combined high-resolution genotyping and geospatial analysis reveals modes of endemic urban typhoid fever transmission

S. Baker, K. E. Holt, A. C. A. Clements, A. Karkey, A. Arjyal, M. F. Boni, S. Dongol, N. Hammond, S. Koirala, P. T. Duy, T. V. T. Nga, J. I. Campbell (+4 others)
2011 Open Biology  
Detection of spatial clustering The scale and significance of clustering of typhoid fever cases with S. Typhi and S.  ...  Duplicates were dispatched for secondary verification at the microbiology laboratory at Oxford University Clinical Research Unit in Ho Chi Minh City and were stored until DNA extraction at 2808C in 20  ... 
doi:10.1098/rsob.110008 pmid:22645647 pmcid:PMC3352080 fatcat:jz5zmgq4obcfrfzx6u4xys5ina

Synthetic Disinformation Attacks on Automated Fact Verification Systems

Yibing Du, Antoine Bosselut, Christopher D. Manning
2022 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Automated fact-checking is a needed technology to curtail the spread of online misinformation.  ...  repository available to the fact-checking system, and ADVERSARIAL MODIFICATION, where existing evidence source documents in the repository are automatically altered.  ...  Acknowledgements We thank the anonymous reviewers for their feedback that improved this paper, Shelby Grossman and the members of the Stanford SNAP and NLP groups for helpful discussions, and Tuhin Chakrabarty  ... 
doi:10.1609/aaai.v36i10.21302 fatcat:z52gxejz6nfh5lqzyvtqudwas4

UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification [article]

Andreas Hanselowski, Hao Zhang, Zile Li, Daniil Sorokin, Benjamin Schiller, Claudia Schulz, Iryna Gurevych
2019 arXiv   pre-print
The shared task organizers provide a large-scale dataset for the consecutive steps involved in claim verification, in particular, document retrieval, fact extraction, and claim classification.  ...  The Fact Extraction and VERification (FEVER) shared task was launched to support the development of systems able to verify claims by extracting supporting or refuting facts from raw text.  ...  The organizers of the FEVER shared task constructed a large-scale dataset (Thorne et al., 2018) based on Wikipedia.  ... 
arXiv:1809.01479v5 fatcat:4axndjg6cvbv7jctnsfgbgtoda

FaVIQ: FAct Verification from Information-seeking Questions [article]

Jungsoo Park, Sewon Min, Jaewoo Kang, Luke Zettlemoyer, Hannaneh Hajishirzi
2022 arXiv   pre-print
Despite significant interest in developing general purpose fact checking models, it is challenging to construct a large-scale fact verification dataset with realistic real-world claims.  ...  In this paper, we construct a large-scale challenging fact verification dataset called FAVIQ, consisting of 188k claims derived from an existing corpus of ambiguous information-seeking questions.  ...  Acknowledgements We thank Dave Wadden, James Thorn and Jinhyuk Lee for discussion and feedback on the paper. We thank James Lee, Skyler Hallinan and Sourojit Ghosh for their help in data validation.  ... 
arXiv:2107.02153v2 fatcat:7ho4vjabyvgadclbyflitvlsiy

Dataset of Fake News Detection and Fact Verification: A Survey [article]

Taichi Murayama
2021 arXiv   pre-print
We surveyed 118 datasets related to fake news research on a large scale from three perspectives: (1) fake news detection, (2) fact verification, and (3) other tasks; for example, the analysis of fake news  ...  The rapid increase in fake news, which causes significant damage to society, triggers many fake news related studies, including the development of fake news detection and fact verification techniques.  ...  For example, the Fact Extraction and VERification (FEVER) task [213] , which is the biggest competition for fact verification, requires combining information from multiple documents and sentences for  ... 
arXiv:2111.03299v1 fatcat:noyhfssx7fe6rhsndcjosbi7oi
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