@inproceedings{aly-etal-2021-fact,
title = "The Fact Extraction and {VER}ification Over Unstructured and Structured information ({FEVEROUS}) Shared Task",
author = "Aly, Rami and
Guo, Zhijiang and
Schlichtkrull, Michael Sejr and
Thorne, James and
Vlachos, Andreas and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Mittal, Arpit",
editor = "Aly, Rami and
Christodoulopoulos, Christos and
Cocarascu, Oana and
Guo, Zhijiang and
Mittal, Arpit and
Schlichtkrull, Michael and
Thorne, James and
Vlachos, Andreas",
booktitle = "Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)",
month = nov,
year = "2021",
address = "Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.fever-1.1",
doi = "10.18653/v1/2021.fever-1.1",
pages = "1--13",
abstract = "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). Compared to the FEVER 2018 shared task, the main challenge is the addition of structured data (tables and lists) as a source of evidence. The claims in the FEVEROUS dataset can be verified using only structured evidence, only unstructured evidence, or a mixture of both. Submissions are evaluated using the FEVEROUS score that combines label accuracy and evidence retrieval. Unlike FEVER 2018, FEVEROUS requires partial evidence to be returned for NotEnoughInfo claims, and the claims are longer and thus more complex. The shared task received 13 entries, six of which were able to beat the baseline system. The winning team was {``}Bust a move!{''}, achieving a FEVEROUS score of 27{\%} (+9{\%} compared to the baseline). In this paper we describe the shared task, present the full results and highlight commonalities and innovations among the participating systems.",
}
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<abstract>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). Compared to the FEVER 2018 shared task, the main challenge is the addition of structured data (tables and lists) as a source of evidence. The claims in the FEVEROUS dataset can be verified using only structured evidence, only unstructured evidence, or a mixture of both. Submissions are evaluated using the FEVEROUS score that combines label accuracy and evidence retrieval. Unlike FEVER 2018, FEVEROUS requires partial evidence to be returned for NotEnoughInfo claims, and the claims are longer and thus more complex. The shared task received 13 entries, six of which were able to beat the baseline system. The winning team was “Bust a move!”, achieving a FEVEROUS score of 27% (+9% compared to the baseline). In this paper we describe the shared task, present the full results and highlight commonalities and innovations among the participating systems.</abstract>
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%0 Conference Proceedings
%T The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task
%A Aly, Rami
%A Guo, Zhijiang
%A Schlichtkrull, Michael Sejr
%A Thorne, James
%A Vlachos, Andreas
%A Christodoulopoulos, Christos
%A Cocarascu, Oana
%A Mittal, Arpit
%Y Aly, Rami
%Y Christodoulopoulos, Christos
%Y Cocarascu, Oana
%Y Guo, Zhijiang
%Y Mittal, Arpit
%Y Schlichtkrull, Michael
%Y Thorne, James
%Y Vlachos, Andreas
%S Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
%D 2021
%8 November
%I Association for Computational Linguistics
%C Dominican Republic
%F aly-etal-2021-fact
%X 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). Compared to the FEVER 2018 shared task, the main challenge is the addition of structured data (tables and lists) as a source of evidence. The claims in the FEVEROUS dataset can be verified using only structured evidence, only unstructured evidence, or a mixture of both. Submissions are evaluated using the FEVEROUS score that combines label accuracy and evidence retrieval. Unlike FEVER 2018, FEVEROUS requires partial evidence to be returned for NotEnoughInfo claims, and the claims are longer and thus more complex. The shared task received 13 entries, six of which were able to beat the baseline system. The winning team was “Bust a move!”, achieving a FEVEROUS score of 27% (+9% compared to the baseline). In this paper we describe the shared task, present the full results and highlight commonalities and innovations among the participating systems.
%R 10.18653/v1/2021.fever-1.1
%U https://aclanthology.org/2021.fever-1.1
%U https://doi.org/10.18653/v1/2021.fever-1.1
%P 1-13
Markdown (Informal)
[The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task](https://aclanthology.org/2021.fever-1.1) (Aly et al., FEVER 2021)
ACL